data
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[
{
"id": "1",
"title": "FOUNDATIONS_OF_CONVERSATIONAL_AI_DESIGN",
"context": "Module Code - Title: CE2002 - FOUNDATIONS OF CONVERSATIONAL AI DESIGN Prerequisite Modules: Rationale and Purpose of the Module: Conversational Artificial Intelligence is the software and processes by which speech is transformed into input for computers and smart devices. This module will provide students with a use case based approach to the Design of modern Conversational AI systems.",
"question": "What is the module foundations of conversational AI design about?",
"answers": [
{
"text": "software and processes by which speech is transformed into input for computers and smart devices",
"answer_start": 175
}
]
},
{
"id": "2",
"title": "FOUNDATIONS_OF_CONVERSATIONAL_AI_DESIGN",
"context": "Module Code - Title: CE2002 - FOUNDATIONS OF CONVERSATIONAL AI DESIGN Prerequisite Modules: Rationale and Purpose of the Module: Conversational Artificial Intelligence is the software and processes by which speech is transformed into input for computers and smart devices.This module will provide students with a use case based approach to the Design of modern Conversational AI systems.",
"question": "What will I be able to do in the foundations of conversational AI design module?",
"answers": [
{
"text": "use case based approach to the Design of modern Conversational AI systems",
"answer_start": 313
}
]
},
{
"id": "3",
"title": "FOUNDATIONS_OF_CONVERSATIONAL_AI_DESIGN",
"context": "Module Code - Title: CE2002 - FOUNDATIONS OF CONVERSATIONAL AI DESIGN Prerequisite Modules: Rationale and Purpose of the Module: Conversational Artificial Intelligence is the software and processes by which speech is transformed into input for computers and smart devices.This module will provide students with a use case based approach to the Design of modern Conversational AI systems.",
"question": "What is the code of the foundations of conversational AI design module? ",
"answers": [
{
"text": "CE2002",
"answer_start": 21
}
]
},
{
"id": "4",
"title": "FOUNDATIONS_OF_CONVERSATIONAL_AI_DESIGN",
"context": "Syllabus: Requirements analysis for systems that are based on Human Computer Spoken word interaction.Conversation flow design. The fundamental components of a conversation tree. Branch and bound techniques. Use case based example CAI specification that classifies Agents, Intents, Entities, Contexts, Interactions, reasoning and responses in enterprise level Conversational AI, (CAI) ecosystems. Scripted versus CAI systems for Human Computer spoken word interaction.Students will build a functioning CAI system using an enterprise level design tool. Students will be required to implement a Conceive Design Implement Operate (CDIO) approach to the construction of their CAI system.",
"question": "What use case will I do in the foundations of conversational AI design module?",
"answers": [
{
"text": "Use case based example CAI specification",
"answer_start": 207
}
]
},
{
"id": "5",
"title": "FOUNDATIONS_OF_CONVERSATIONAL_AI_DESIGN",
"context": "Syllabus: Requirements analysis for systems that are based on Human Computer Spoken word interaction.Conversation flow design. The fundamental components of a conversation tree. Branch and bound techniques.Use case based example CAI specification that classifies Agents, Intents, Entities, Contexts, Interactions, reasoning and responses in enterprise level Conversational AI, (CAI) ecosystems. Scripted versus CAI systems for Human Computer spoken word interaction.Students will build a functioning CAI system using an enterprise level design tool. Students will be required to implement a Conceive Design Implement Operate (CDIO) approach to the construction of their CAI system.",
"question": "What will I be able to build in the foundations of conversational AI design module?",
"answers": [
{
"text": "Students will build a functioning CAI system using an enterprise level design tool",
"answer_start": 466
}
]
},
{
"id": "6",
"title": "FOUNDATIONS_OF_CONVERSATIONAL_AI_DESIGN",
"context": "Syllabus: Requirements analysis for systems that are based on Human Computer Spoken word interaction.Conversation flow design. The fundamental components of a conversation tree. Branch and bound techniques.Use case based example CAI specification that classifies Agents, Intents, Entities, Contexts, Interactions, reasoning and responses in enterprise level Conversational AI, (CAI) ecosystems. Scripted versus CAI systems for Human Computer spoken word interaction.Students will build a functioning CAI system using an enterprise level design tool. Students will be required to implement a Conceive Design Implement Operate (CDIO) approach to the construction of their CAI system.",
"question": "What will I be required to do in the foundations of conversational AI design module?",
"answers": [
{
"text": "implement a Conceive Design Implement Operate",
"answer_start": 579
}
]
},
{
"id": "7",
"title": "FOUNDATIONS_OF_CONVERSATIONAL_AI_DESIGN",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) On successful completion of this module, students will be able to: Design a functional conversation narrative that can be successfully implemented in a Conversational AI (CAI) use case. Build a basic CAI application using an enterprise level design tool. Determine the individual component subsystems of a CAI design. Affective (Attitudes and Values) On successful completion of this module, students will be able to: Explain the difference between a scripted Chatbot and a conversational artificial intelligence system. Critically assess the performance of a functioning CAI system. Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Materials will be delivered in a blended manner through weekly pre-recorded sessions and live class sessions. The module material will include video recordings as well as readings, exercises, and assignments. The focus is on the reduction of theory to practice so there will be a strong emphasis on developing participants' practical skills. Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Jurafsky, Daniel. and Martin, James H. (2008) Speech and Language Processing: International Edition, Pearson Other Texts: Kamath, Uday. and Liu, John. (2020) Deep Learning for NLP and Speech Recognition, Springer Programmes Semester(s) Module is Offered: Spring",
"question": "What will I be able to learn in the foundations of conversational AI design module?",
"answers": [
{
"text": "Design a functional conversation narrative",
"answer_start": 169
}
]
},
{
"id": "8",
"title": "FOUNDATIONS_OF_CONVERSATIONAL_AI_DESIGN",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) On successful completion of this module, students will be able to: Design a functional conversation narrative that can be successfully implemented in a Conversational AI (CAI) use case. Build a basic CAI application using an enterprise level design tool. Determine the individual component subsystems of a CAI design. Affective (Attitudes and Values) On successful completion of this module, students will be able to: Explain the difference between a scripted Chatbot and a conversational artificial intelligence system. Critically assess the performance of a functioning CAI system. Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Materials will be delivered in a blended manner through weekly pre-recorded sessions and live class sessions. The module material will include video recordings as well as readings, exercises, and assignments. The focus is on the reduction of theory to practice so there will be a strong emphasis on developing participants' practical skills. Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Jurafsky, Daniel. and Martin, James H. (2008) Speech and Language Processing: International Edition, Pearson Other Texts: Kamath, Uday. and Liu, John. (2020) Deep Learning for NLP and Speech Recognition, Springer Programmes Semester(s) Module is Offered: Spring",
"question": "How the conversational AI design module will be delivered?",
"answers": [
{
"text": "weekly pre-recorded sessions and live class sessions",
"answer_start": 861
}
]
},
{
"id": "9",
"title": "FOUNDATIONS_OF_CONVERSATIONAL_AI_DESIGN",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) On successful completion of this module, students will be able to: Design a functional conversation narrative that can be successfully implemented in a Conversational AI (CAI) use case. Build a basic CAI application using an enterprise level design tool. Determine the individual component subsystems of a CAI design. Affective (Attitudes and Values) On successful completion of this module, students will be able to: Explain the difference between a scripted Chatbot and a conversational artificial intelligence system. Critically assess the performance of a functioning CAI system. Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Materials will be delivered in a blended manner through weekly pre-recorded sessions and live class sessions. The module material will include video recordings as well as readings, exercises, and assignments. The focus is on the reduction of theory to practice so there will be a strong emphasis on developing participants' practical skills. Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Jurafsky, Daniel. and Martin, James H. (2008) Speech and Language Processing: International Edition, Pearson Other Texts: Kamath, Uday. and Liu, John. (2020) Deep Learning for NLP and Speech Recognition, Springer Programmes Semester(s) Module is Offered: Spring",
"question": "In what semester the foundations of conversational AI design module takes place?",
"answers": [
{
"text": "Spring",
"answer_start": 1430
}
]
},
{
"id": "10",
"title": "THEORY_AND_PRACTICE_FOR_CONVERSATIONAL_AI",
"context": "Module Code - Title: CE2003 - THEORY AND PRACTICE FOR CONVERSATIONAL AI Prerequisite Modules: Rationale and Purpose of the Module: Conversational Artificial Intelligence (CAI) is the software and processes by which speech is transformed into input for computers and smart devices. This module will provide students with practical insights regarding the theoretical concepts that underpin modern Conversational AI systems. Syllabus: Introduction to machine learning for Conversational AI, (CAI). Scripted versus CAI systems for Human Computer spoken word interaction. Neural Networks for CAI. The definition and application of RNN, CNN, DNN, xNN subsystems to CAI. Speech recognition, language modeling and language decoding for CAI. Evaluation of Speech recognition tools. Data collection and labelling for training in CAI. Bag of words testing. An introduction to N-gram based modelling of speech. Evaluation of intent in CAI. Development of training sentences. Evaluation of Semantics, context and embedding in CAI systems. Dialog management: Introduction to Reasoning and Response generation in computer-based CAI systems.",
"question": "What is the code of the theory and practice for conversational AI module?",
"answers": [
{
"text": "CE2003",
"answer_start": 21
}
]
},
{
"id": "11",
"title": "THEORY_AND_PRACTICE_FOR_CONVERSATIONAL_AI",
"context": "Module Code - Title: CE2003 - THEORY AND PRACTICE FOR CONVERSATIONAL AI Prerequisite Modules: Rationale and Purpose of the Module: Conversational Artificial Intelligence (CAI) is the software and processes by which speech is transformed into input for computers and smart devices. This module will provide students with practical insights regarding the theoretical concepts that underpin modern Conversational AI systems. Syllabus: Introduction to machine learning for Conversational AI, (CAI). Scripted versus CAI systems for Human Computer spoken word interaction. Neural Networks for CAI. The definition and application of RNN, CNN, DNN, xNN subsystems to CAI. Speech recognition, language modeling and language decoding for CAI. Evaluation of Speech recognition tools. Data collection and labelling for training in CAI. Bag of words testing. An introduction to N-gram based modelling of speech. Evaluation of intent in CAI. Development of training sentences. Evaluation of Semantics, context and embedding in CAI systems. Dialog management: Introduction to Reasoning and Response generation in computer-based CAI systems.",
"question": "What neural networks definitions will I learn in the theory and practice for conversational AI about?",
"answers": [
{
"text": "RNN, CNN, DNN, xNN subsystems",
"answer_start": 626
}
]
},
{
"id": "12",
"title": "THEORY_AND_PRACTICE_FOR_CONVERSATIONAL_AI",
"context": "Module Code - Title: CE2003 - THEORY AND PRACTICE FOR CONVERSATIONAL AI Prerequisite Modules: Rationale and Purpose of the Module: Conversational Artificial Intelligence (CAI) is the software and processes by which speech is transformed into input for computers and smart devices. This module will provide students with practical insights regarding the theoretical concepts that underpin modern Conversational AI systems. Syllabus: Introduction to machine learning for Conversational AI, (CAI). Scripted versus CAI systems for Human Computer spoken word interaction. Neural Networks for CAI. The definition and application of RNN, CNN, DNN, xNN subsystems to CAI. Speech recognition, language modeling and language decoding for CAI. Evaluation of Speech recognition tools. Data collection and labelling for training in CAI. Bag of words testing. An introduction to N-gram based modelling of speech. Evaluation of intent in CAI. Development of training sentences. Evaluation of Semantics, context and embedding in CAI systems. Dialog management: Introduction to Reasoning and Response generation in computer-based CAI systems.",
"question": "What will I learn in the theory and practice for conversational AI about?",
"answers": [
{
"text": "theoretical concepts that underpin modern Conversational AI systems",
"answer_start": 353
}
]
},
{
"id": "13",
"title": "THEORY_AND_PRACTICE_FOR_CONVERSATIONAL_AI",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) On successful completion of this module, students will be able to: Identify the fundamental components of a Conversational AI (CAI) system. Decode elementary speech patterns for use in CAI systems.Determine intent from uttered speech data in CAI systems. Explain how computer controlled responses are generated in modern CAI systems. Affective (Attitudes and Values) On successful completion of this module, students will be able to:Explain the difference between a scripted Chatbot and a conversational artificial intelligence system.Characterise the performance of a CAI system.Demonstrate an understanding of how useful information is extracted from raw speech in CAI systems.Psychomotor (Physical Skills). How the Module will be Taught and what will be the Learning Experiences of the Students: Materials will be delivered in a blended manner through weekly pre-recorded sessions and live class sessions. The course material will include video recordings as well as readings, exercises, and assignments. The focus is on the reduction of theory to practice so there will be a strong emphasis on developing participants' practical skills.Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Jurafsky, Daniel, & Martin, James H. (2008) Speech and Language Processing: International Version: an Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, Pearson Uday, K. and Liu, J. (2020) Deep Learning for NLP and Speech Recognition, Springer Other Texts: Wu Chou,& Biing-Hwang Juang (2005) Pattern Recognition in Speech and Language Processing (Electrical Engineering & Applied Signal Processing Series), CRC Press Rabiner, L. and Schafer, R. (2010) Theory and Applications of Digital Speech Processing, Pearson Programmes Semester(s) Module is Offered: Spring",
"question": "What will I be able to explain in the theory and practice for conversational AI module?",
"answers": [
{
"text": "the difference between a scripted Chatbot and a conversational artificial intelligence system",
"answer_start": 544
}
]
},
{
"id": "14",
"title": "THEORY_AND_PRACTICE_FOR_CONVERSATIONAL_AI",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) On successful completion of this module, students will be able to: Identify the fundamental components of a Conversational AI (CAI) system. Decode elementary speech patterns for use in CAI systems.Determine intent from uttered speech data in CAI systems. Explain how computer controlled responses are generated in modern CAI systems. Affective (Attitudes and Values) On successful completion of this module, students will be able to:Explain the difference between a scripted Chatbot and a conversational artificial intelligence system.Characterise the performance of a CAI system.Demonstrate an understanding of how useful information is extracted from raw speech in CAI systems.Psychomotor (Physical Skills). How the Module will be Taught and what will be the Learning Experiences of the Students: Materials will be delivered in a blended manner through weekly pre-recorded sessions and live class sessions. The course material will include video recordings as well as readings, exercises, and assignments. The focus is on the reduction of theory to practice so there will be a strong emphasis on developing participants' practical skills.Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Jurafsky, Daniel, & Martin, James H. (2008) Speech and Language Processing: International Version: an Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, Pearson Uday, K. and Liu, J. (2020) Deep Learning for NLP and Speech Recognition, Springer Other Texts: Wu Chou,& Biing-Hwang Juang (2005) Pattern Recognition in Speech and Language Processing (Electrical Engineering & Applied Signal Processing Series), CRC Press Rabiner, L. and Schafer, R. (2010) Theory and Applications of Digital Speech Processing, Pearson Programmes Semester(s) Module is Offered: Spring",
"question": "What does the course material for the theory and practice for conversational AI module include?",
"answers": [
{
"text": "video recordings as well as readings, exercises, and assignments",
"answer_start": 1045
}
]
},
{
"id": "15",
"title": "THEORY_AND_PRACTICE_FOR_CONVERSATIONAL_AI",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) On successful completion of this module, students will be able to: Identify the fundamental components of a Conversational AI (CAI) system. Decode elementary speech patterns for use in CAI systems.Determine intent from uttered speech data in CAI systems. Explain how computer controlled responses are generated in modern CAI systems. Affective (Attitudes and Values) On successful completion of this module, students will be able to:Explain the difference between a scripted Chatbot and a conversational artificial intelligence system.Characterise the performance of a CAI system.Demonstrate an understanding of how useful information is extracted from raw speech in CAI systems.Psychomotor (Physical Skills). How the Module will be Taught and what will be the Learning Experiences of the Students: Materials will be delivered in a blended manner through weekly pre-recorded sessions and live class sessions. The course material will include video recordings as well as readings, exercises, and assignments. The focus is on the reduction of theory to practice so there will be a strong emphasis on developing participants' practical skills.Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Jurafsky, Daniel, & Martin, James H. (2008) Speech and Language Processing: International Version: an Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, Pearson Uday, K. and Liu, J. (2020) Deep Learning for NLP and Speech Recognition, Springer Other Texts: Wu Chou,& Biing-Hwang Juang (2005) Pattern Recognition in Speech and Language Processing (Electrical Engineering & Applied Signal Processing Series), CRC Press Rabiner, L. and Schafer, R. (2010) Theory and Applications of Digital Speech Processing, Pearson Programmes Semester(s) Module is Offered: Spring",
"question": "In what semester the theory and practice for conversational AI module takes place?",
"answers": [
{
"text": "Spring",
"answer_start": 1603
}
]
},
{
"id": "16",
"title": "PROGRAMMING_FOUNDATIONS_FOR_CONVERSATIONAL_AI",
"context": "Module Code - Title: CE2012 - PROGRAMMING FOUNDATIONS FOR CONVERSATIONAL AI Prerequisite Modules: Rationale and Purpose of the Module: Conversational Artificial Intelligence is the software and processes by which speech is transformed into input for computers and smart devices.This module will provide students with an overview of the programming foundations that are used to engineer modern Conversational AI (CAI) systems.Syllabus:An introduction to scripting Languages and Environments for Scientific Computing: An introduction to the syntax of one modern scripting languages (e.g. Python, Julia or the latest equivalent) and environments. An introduction to Numerical issues in CAI systems. The bag of words test and the generation of analysis vectors. Numerics support in typical scientific scripting (e.g., Numpy/Scipy). Matrices and linear algebra Graphics and Scientific Visualization of words and sentences in CAI systems: Using scripting languages to build scientific visualizations (scalar, vector fields). Random Numbers and Probability: Random number generation: Classification in CAI systems. Simple classifiers. K-means. Linear classifiers: Perceptron. Least squares and gradient descent. Modern optimization for neural networks: Nesterov momentum, the ADAM optimizer.",
"question": "What is the code of the programming foundations for conversational AI module?",
"answers": [
{
"text": "CE2012",
"answer_start": 21
}
]
},
{
"id": "17",
"title": "PROGRAMMING_FOUNDATIONS_FOR_CONVERSATIONAL_AI",
"context": "Module Code - Title: CE2012 - PROGRAMMING FOUNDATIONS FOR CONVERSATIONAL AI Prerequisite Modules: Rationale and Purpose of the Module: Conversational Artificial Intelligence is the software and processes by which speech is transformed into input for computers and smart devices.This module will provide students with an overview of the programming foundations that are used to engineer modern Conversational AI (CAI) systems.Syllabus:An introduction to scripting Languages and Environments for Scientific Computing: An introduction to the syntax of one modern scripting languages (e.g. Python, Julia or the latest equivalent) and environments. An introduction to Numerical issues in CAI systems. The bag of words test and the generation of analysis vectors. Numerics support in typical scientific scripting (e.g., Numpy/Scipy). Matrices and linear algebra Graphics and Scientific Visualization of words and sentences in CAI systems: Using scripting languages to build scientific visualizations (scalar, vector fields). Random Numbers and Probability: Random number generation: Classification in CAI systems. Simple classifiers. K-means. Linear classifiers: Perceptron. Least squares and gradient descent. Modern optimization for neural networks: Nesterov momentum, the ADAM optimizer.",
"question": "What programming languages will I learn in the programming foundations for conversational AI module?",
"answers": [
{
"text": "Python, Julia or the latest equivalent",
"answer_start": 586
}
]
},
{
"id": "18",
"title": "PROGRAMMING_FOUNDATIONS_FOR_CONVERSATIONAL_AI",
"context": "Module Code - Title: CE2012 - PROGRAMMING FOUNDATIONS FOR CONVERSATIONAL AI Prerequisite Modules: Rationale and Purpose of the Module: Conversational Artificial Intelligence is the software and processes by which speech is transformed into input for computers and smart devices.This module will provide students with an overview of the programming foundations that are used to engineer modern Conversational AI (CAI) systems.Syllabus:An introduction to scripting Languages and Environments for Scientific Computing: An introduction to the syntax of one modern scripting languages (e.g. Python, Julia or the latest equivalent) and environments. An introduction to Numerical issues in CAI systems. The bag of words test and the generation of analysis vectors. Numerics support in typical scientific scripting (e.g., Numpy/Scipy). Matrices and linear algebra Graphics and Scientific Visualization of words and sentences in CAI systems: Using scripting languages to build scientific visualizations (scalar, vector fields). Random Numbers and Probability: Random number generation: Classification in CAI systems. Simple classifiers. K-means. Linear classifiers: Perceptron. Least squares and gradient descent. Modern optimization for neural networks: Nesterov momentum, the ADAM optimizer.",
"question": "What optimization methods will I learn in the programming foundations for conversational AI module?",
"answers": [
{
"text": "Nesterov momentum, the ADAM optimizer",
"answer_start": 1248
}
]
},
{
"id": "19",
"title": "PROGRAMMING_FOUNDATIONS_FOR_CONVERSATIONAL_AI",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) On successful completion of this module, students will be able to: Given a target programming language, demonstrate proficiencies in the syntax necessary to implement standard programming constructs in CAI systems. Given a basic bag of words dataset, construct simple programmes to perform simple analysis operations. Given a CAI problem, identify and evaluate the outputs through appropriate visualisation. Given a CAI problem, discriminate and select basic approaches to scientific computing. Given an appropriate bag of words data set, the student will write a program to process the data e.g. find the principal components.Affective (Attitudes and Values) On successful completion of this module, students will be able to: Given datasets, demonstrate knowledge of how to question whether the data is representative and how to attempt to address any biases. Given a bag of words to investigate, demonstrate knowledge of how to identify and discuss any potential ethical considerations that might obtain. Through the use of appropriate outputs including visualisation, demonstrate ability to identify a basic intent from a bag of words dataset. Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Materials will be delivered in a blended manner through weekly pre-recorded sessions and live class sessions. The module material will include video recordings as well as readings, exercises, and assignments. The focus is on the reduction of theory to practice so there will be a strong emphasis on developing participants practical skills. Research Findings Incorporated in to the Syllabus (If Relevant):Prime Texts: Langtangen (2016) A Primer on Scientific Programming with Python , Springer Other Texts: Beazley (2016) Machine Learning , Cambridge Goodfellow & Bengio (2014) Deep Learning , MIT Press Lane Hobson (Author), Howard Cole (Author), Hapke Hannes (Author) (2019) Natural Language Processing in Action: Understanding, analyzing, and generating text with Python , Manning Programmes Semester(s) Module is Offered: Spring",
"question": "When the programming foundations for conversational AI module takes place?",
"answers": [
{
"text": "Spring",
"answer_start": 1855
}
]
},
{
"id": "20",
"title": "PROGRAMMING_FOUNDATIONS_FOR_CONVERSATIONAL_AI",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) On successful completion of this module, students will be able to: Given a target programming language, demonstrate proficiencies in the syntax necessary to implement standard programming constructs in CAI systems. Given a basic bag of words dataset, construct simple programmes to perform simple analysis operations. Given a CAI problem, identify and evaluate the outputs through appropriate visualisation. Given a CAI problem, discriminate and select basic approaches to scientific computing. Given an appropriate bag of words data set, the student will write a program to process the data e.g. find the principal components.Affective (Attitudes and Values) On successful completion of this module, students will be able to: Given datasets, demonstrate knowledge of how to question whether the data is representative and how to attempt to address any biases. Given a bag of words to investigate, demonstrate knowledge of how to identify and discuss any potential ethical considerations that might obtain. Through the use of appropriate outputs including visualisation, demonstrate ability to identify a basic intent from a bag of words dataset. Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Materials will be delivered in a blended manner through weekly pre-recorded sessions and live class sessions. The module material will include video recordings as well as readings, exercises, and assignments. The focus is on the reduction of theory to practice so there will be a strong emphasis on developing participants practical skills. Research Findings Incorporated in to the Syllabus (If Relevant):Prime Texts: Langtangen (2016) A Primer on Scientific Programming with Python , Springer Other Texts: Beazley (2016) Machine Learning , Cambridge Goodfellow & Bengio (2014) Deep Learning , MIT Press Lane Hobson (Author), Howard Cole (Author), Hapke Hannes (Author) (2019) Natural Language Processing in Action: Understanding, analyzing, and generating text with Python , Manning Programmes Semester(s) Module is Offered: Spring",
"question": "Is the programming foundations for conversational AI module practical?",
"answers": [
{
"text": "there will be a strong emphasis on developing participants practical skills",
"answer_start": 1633
}
]
},
{
"id": "21",
"title": "PROGRAMMING_FOUNDATIONS_FOR_CONVERSATIONAL_AI",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) On successful completion of this module, students will be able to: Given a target programming language, demonstrate proficiencies in the syntax necessary to implement standard programming constructs in CAI systems. Given a basic bag of words dataset, construct simple programmes to perform simple analysis operations. Given a CAI problem, identify and evaluate the outputs through appropriate visualisation. Given a CAI problem, discriminate and select basic approaches to scientific computing. Given an appropriate bag of words data set, the student will write a program to process the data e.g. find the principal components.Affective (Attitudes and Values) On successful completion of this module, students will be able to: Given datasets, demonstrate knowledge of how to question whether the data is representative and how to attempt to address any biases. Given a bag of words to investigate, demonstrate knowledge of how to identify and discuss any potential ethical considerations that might obtain. Through the use of appropriate outputs including visualisation, demonstrate ability to identify a basic intent from a bag of words dataset. Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Materials will be delivered in a blended manner through weekly pre-recorded sessions and live class sessions. The module material will include video recordings as well as readings, exercises, and assignments. The focus is on the reduction of theory to practice so there will be a strong emphasis on developing participants practical skills. Research Findings Incorporated in to the Syllabus (If Relevant):Prime Texts: Langtangen (2016) A Primer on Scientific Programming with Python , Springer Other Texts: Beazley (2016) Machine Learning , Cambridge Goodfellow & Bengio (2014) Deep Learning , MIT Press Lane Hobson (Author), Howard Cole (Author), Hapke Hannes (Author) (2019) Natural Language Processing in Action: Understanding, analyzing, and generating text with Python , Manning Programmes Semester(s) Module is Offered: Spring",
"question": "What textbook I need to read for the programming foundations for conversational AI module?",
"answers": [
{
"text": "A Primer on Scientific Programming with Python",
"answer_start": 1806
}
]
},
{
"id": "22",
"title": "INDUSTRY_PROJECT",
"context": "Module Code - Title: CE2013 - INDUSTRY PROJECT Prerequisite Modules: Rationale and Purpose of the Module: To enable the student to combine previously learned course material with their individual talents in order to solve real-life industry projects. To develop in the students the ability to organise and direct their own work and to present this work in written and verbal format in a proper manner. Syllabus: [Project Management] Students undertaking of this module must implement a project plan outlining various phases of the project. Estimation of goals and task scheduling must analysed, identified and prioritised. [Independent Research] Students must demonstrate an ability to research and investigate aspects of the project independently. A proven aptitude in coordination of, and active involvement in, information gathering, analysis and formal presentation of findings must be exhibited [Knowledge Implementation] Implementation of the project must incorporate all modules associated within the project stream. In this manner students are guaranteed to be equipped with the essential tools to acquire further knowledge and insight. [Documentation Proficiency] As part of the module criteria a report must be completed to support the project. This should include the initial scope, methodologies applied and tools and techniques employed, in addition to the motivations for the project.",
"question": "What is the code of the industry project module?",
"answers": [
{
"text": "CE2013",
"answer_start": 21
}
]
},
{
"id": "23",
"title": "INDUSTRY_PROJECT",
"context": "Module Code - Title: CE2013 - INDUSTRY PROJECT Prerequisite Modules: Rationale and Purpose of the Module: To enable the student to combine previously learned course material with their individual talents in order to solve real-life industry projects. To develop in the students the ability to organise and direct their own work and to present this work in written and verbal format in a proper manner. Syllabus: [Project Management] Students undertaking of this module must implement a project plan outlining various phases of the project. Estimation of goals and task scheduling must analysed, identified and prioritised. [Independent Research] Students must demonstrate an ability to research and investigate aspects of the project independently. A proven aptitude in coordination of, and active involvement in, information gathering, analysis and formal presentation of findings must be exhibited [Knowledge Implementation] Implementation of the project must incorporate all modules associated within the project stream. In this manner students are guaranteed to be equipped with the essential tools to acquire further knowledge and insight. [Documentation Proficiency] As part of the module criteria a report must be completed to support the project. This should include the initial scope, methodologies applied and tools and techniques employed, in addition to the motivations for the project.",
"question": "How the research is taught in the industry project module?",
"answers": [
{
"text": "Students must demonstrate an ability to research and investigate aspects of the project independently",
"answer_start": 646
}
]
},
{
"id": "24",
"title": "INDUSTRY_PROJECT",
"context": "Module Code - Title: CE2013 - INDUSTRY PROJECT Prerequisite Modules: Rationale and Purpose of the Module: To enable the student to combine previously learned course material with their individual talents in order to solve real-life industry projects. To develop in the students the ability to organise and direct their own work and to present this work in written and verbal format in a proper manner. Syllabus: [Project Management] Students undertaking of this module must implement a project plan outlining various phases of the project. Estimation of goals and task scheduling must analysed, identified and prioritised. [Independent Research] Students must demonstrate an ability to research and investigate aspects of the project independently. A proven aptitude in coordination of, and active involvement in, information gathering, analysis and formal presentation of findings must be exhibited [Knowledge Implementation] Implementation of the project must incorporate all modules associated within the project stream. In this manner students are guaranteed to be equipped with the essential tools to acquire further knowledge and insight. [Documentation Proficiency] As part of the module criteria a report must be completed to support the project. This should include the initial scope, methodologies applied and tools and techniques employed, in addition to the motivations for the project.",
"question": "What is the purpose of the industry project module?",
"answers": [
{
"text": "To enable the student to combine previously learned course material with their individual talents",
"answer_start": 106
}
]
},
{
"id": "25",
"title": "INDUSTRY_PROJECT",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) On successful completion of this module, students will be able to: Develop and present a project plan, modularise the project into work packages, and identify the resources required to complete work packages.Demonstrate the ability to develop solutions to moderately complex problems. Work as an individual and within a team, with support from a supervisor, drawing on knowledge and experience to solve problems. Report the work done on the project, including references to previous work, and recommendations for future work.Affective (Attitudes and Values) On successful completion of this module, students will be able to: foster the ability to recognise the potential for investigation in existing work practices provide students with a awareness of the potential research has to generate ideas and solve problems in an industrial setting Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: This module will be thought in a flexible mode; content delivery will be through online and offline video classes and short learning materials.Each student is required to obtain a suitable project based on an industrial need. Under the supervision of a member of staff, the student will progress along a logical path to resolve the specified problem. Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Wisker, Gina (2009) The undergraduate research handbook, Palgrave MacMillan Thomas, Gary (2017) How to do your research project : a guide for students, Sage Other Texts: Breach, Mark. () Dissertation Writing for Engineers and Scientists, Prentice Hall Robson, Colin () How to do a Research Project. A Guide for Undergraduate Students, Blackwell Publishing Programmes Semester(s) Module is Offered: Autumn Spring Summer Module Leader: [email protected]",
"question": "What will I learn in the industry project module?",
"answers": [
{
"text": "Demonstrate the ability to develop solutions to moderately complex problems",
"answer_start": 310
}
]
},
{
"id": "26",
"title": "INDUSTRY_PROJECT",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) On successful completion of this module, students will be able to: Develop and present a project plan, modularise the project into work packages, and identify the resources required to complete work packages.Demonstrate the ability to develop solutions to moderately complex problems. Work as an individual and within a team, with support from a supervisor, drawing on knowledge and experience to solve problems. Report the work done on the project, including references to previous work, and recommendations for future work.Affective (Attitudes and Values) On successful completion of this module, students will be able to: foster the ability to recognise the potential for investigation in existing work practices provide students with a awareness of the potential research has to generate ideas and solve problems in an industrial setting Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: This module will be thought in a flexible mode; content delivery will be through online and offline video classes and short learning materials.Each student is required to obtain a suitable project based on an industrial need. Under the supervision of a member of staff, the student will progress along a logical path to resolve the specified problem. Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Wisker, Gina (2009) The undergraduate research handbook, Palgrave MacMillan Thomas, Gary (2017) How to do your research project : a guide for students, Sage Other Texts: Breach, Mark. () Dissertation Writing for Engineers and Scientists, Prentice Hall Robson, Colin () How to do a Research Project. A Guide for Undergraduate Students, Blackwell Publishing Programmes Semester(s) Module is Offered: Autumn Spring Summer Module Leader: [email protected]",
"question": "When does the industry project module run?",
"answers": [
{
"text": "Autumn Spring Summer",
"answer_start": 1890
}
]
},
{
"id": "27",
"title": "INDUSTRY_PROJECT",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) On successful completion of this module, students will be able to: Develop and present a project plan, modularise the project into work packages, and identify the resources required to complete work packages.Demonstrate the ability to develop solutions to moderately complex problems. Work as an individual and within a team, with support from a supervisor, drawing on knowledge and experience to solve problems. Report the work done on the project, including references to previous work, and recommendations for future work.Affective (Attitudes and Values) On successful completion of this module, students will be able to: foster the ability to recognise the potential for investigation in existing work practices provide students with a awareness of the potential research has to generate ideas and solve problems in an industrial setting Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: This module will be thought in a flexible mode; content delivery will be through online and offline video classes and short learning materials.Each student is required to obtain a suitable project based on an industrial need. Under the supervision of a member of staff, the student will progress along a logical path to resolve the specified problem. Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Wisker, Gina (2009) The undergraduate research handbook, Palgrave MacMillan Thomas, Gary (2017) How to do your research project : a guide for students, Sage Other Texts: Breach, Mark. () Dissertation Writing for Engineers and Scientists, Prentice Hall Robson, Colin () How to do a Research Project. A Guide for Undergraduate Students, Blackwell Publishing Programmes Semester(s) Module is Offered: Autumn Spring Summer Module Leader: [email protected]",
"question": "How the industry project module will be delivered?",
"answers": [
{
"text": "online and offline video classes and short learning materials",
"answer_start": 1144
}
]
},
{
"id": "28",
"title": "INTRODUCTION_TO_SCIENTIFIC_COMPUTING_FOR_AI",
"context": "Module Code - Title: CE4021 - INTRODUCTION TO SCIENTIFIC COMPUTING FOR AI Prerequisite Modules: Rationale and Purpose of the Module: To prepare students to take a range of Artificial Intelligence related modules by introducing the associated scientific computing, programming language and host platforms. Syllabus: 1. Scripting Languages and Environments for Scientific Computing: Modern scripting languages (e.g. Python, Julia) and environments. 2. Numeric: Numerics support in typical scientific scripting (e.g., Numpy/Scipy). Matrices and linear algebra 3. Graphics and Scientific Visualization: Using scripting languages to build scientific visualizations (scalar, vector fields). 4. Acceleration: Accelerating scientific codes. Threading and parallelism. 5. Random Numbers and Probability: Random number generation: linear congruential generators. Distributions: uniform, normal, etc. Bayesian methods: Gaussian na茂ve Bayes classification. 7. Classifiers and Optimization: Simple classifiers. K-means. Linear classifiers: Perceptron. Least squares and gradient descent. Other cost functions: cross-entropy. Application: training classifiers. Modern optimization for neural networks: Nesterov momentum, ADAM optimizer. 8. Scientific Computing in the Cloud: Docker images. Cloud services. Running scientific code in the cloud.",
"question": "What is the code for the introduction to scientific computing for ai module?",
"answers": [
{
"text": "CE4021",
"answer_start": 21
}
]
},
{
"id": "29",
"title": "INTRODUCTION_TO_SCIENTIFIC_COMPUTING_FOR_AI",
"context": "Module Code - Title: CE4021 - INTRODUCTION TO SCIENTIFIC COMPUTING FOR AI Prerequisite Modules: Rationale and Purpose of the Module: To prepare students to take a range of Artificial Intelligence related modules by introducing the associated scientific computing, programming language and host platforms. Syllabus: 1. Scripting Languages and Environments for Scientific Computing: Modern scripting languages (e.g. Python, Julia) and environments. 2. Numeric: Numerics support in typical scientific scripting (e.g., Numpy/Scipy). Matrices and linear algebra 3. Graphics and Scientific Visualization: Using scripting languages to build scientific visualizations (scalar, vector fields). 4. Acceleration: Accelerating scientific codes. Threading and parallelism. 5. Random Numbers and Probability: Random number generation: linear congruential generators. Distributions: uniform, normal, etc. Bayesian methods: Gaussian na茂ve Bayes classification. 7. Classifiers and Optimization: Simple classifiers. K-means. Linear classifiers: Perceptron. Least squares and gradient descent. Other cost functions: cross-entropy. Application: training classifiers. Modern optimization for neural networks: Nesterov momentum, ADAM optimizer. 8. Scientific Computing in the Cloud: Docker images. Cloud services. Running scientific code in the cloud.",
"question": "Do I need to know any mathematics for the introduction to scientific computing for ai module?",
"answers": [
{
"text": "Matrices and linear algebra",
"answer_start": 531
}
]
},
{
"id": "30",
"title": "INTRODUCTION_TO_SCIENTIFIC_COMPUTING_FOR_AI",
"context": "Module Code - Title: CE4021 - INTRODUCTION TO SCIENTIFIC COMPUTING FOR AI Prerequisite Modules: Rationale and Purpose of the Module: To prepare students to take a range of Artificial Intelligence related modules by introducing the associated scientific computing, programming language and host platforms. Syllabus: 1. Scripting Languages and Environments for Scientific Computing: Modern scripting languages (e.g. Python, Julia) and environments. 2. Numeric: Numerics support in typical scientific scripting (e.g., Numpy/Scipy). Matrices and linear algebra 3. Graphics and Scientific Visualization: Using scripting languages to build scientific visualizations (scalar, vector fields). 4. Acceleration: Accelerating scientific codes. Threading and parallelism. 5. Random Numbers and Probability: Random number generation: linear congruential generators. Distributions: uniform, normal, etc. Bayesian methods: Gaussian na茂ve Bayes classification. 7. Classifiers and Optimization: Simple classifiers. K-means. Linear classifiers: Perceptron. Least squares and gradient descent. Other cost functions: cross-entropy. Application: training classifiers. Modern optimization for neural networks: Nesterov momentum, ADAM optimizer. 8. Scientific Computing in the Cloud: Docker images. Cloud services. Running scientific code in the cloud.",
"question": "What programming languages will I learn in the scientific computing for ai module?",
"answers": [
{
"text": "Python, Julia",
"answer_start": 415
}
]
},
{
"id": "31",
"title": "INTRODUCTION_TO_SCIENTIFIC_COMPUTING_FOR_AI",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) 1. Given a target programming language, the student will become proficient in the syntax necessary to implement standard programming constructs. 2. Given a set of basic scientific problems, the student will construct simple programmes to investigate the problems. 3. Given a scientific problem, the student will identify and evaluate the outputs through appropriate visualisation. 4. Given a scientific problem, the student will discriminate and select basic approaches to scientific computing, including the use of cloud services. 5. Given an appropriate data set, the student will write a program to process the data e.g. find the principal components. 6. Given an image, the student will write a program to implement an operation on the image e.g. dithering to reduce its bit depth. 7. Given a classifier, the student will write a program to implement and analyse it e.g. plot its decision boundary; display an animation of its trajectory of weights over the error surface. Affective (Attitudes and Values) 1. Given datasets, the student will question whether the data is representative and attempt to address any biases 2. Given problems to investigate, the student will identify and discuss any potential ethical considerations. 3. On completion of an investigation using appropriate outputs including visualisation, the student will be able to defend the approach adopted. Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: The module will be delivered using a blended learning approach using on-line lectures, labs and tutorials. Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Langtangen (2016) A Primer on Scientific Programming with Python, Springer Other Texts: Beazley (2016) Python Essential Reference, 4th ed., O'Reilly Flach (2012) Machine Learning, Cambridge Goodfellow & Bengio (2014) Deep Learning, MIT Press Marsland (2014) Machine Learning: An Algorithmic Perspective, CRC Press Foster & Gannon (2017) Cloud Computing for Science and Engineering, MIT Press Programmes Semester(s) Module is Offered: Autumn Module Leader: [email protected]",
"question": "How the scientific computing for ai module will be taught?",
"answers": [
{
"text": "on-line lectures, labs and tutorials",
"answer_start": 1669
}
]
},
{
"id": "32",
"title": "INTRODUCTION_TO_SCIENTIFIC_COMPUTING_FOR_AI",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) 1. Given a target programming language, the student will become proficient in the syntax necessary to implement standard programming constructs. 2. Given a set of basic scientific problems, the student will construct simple programmes to investigate the problems. 3. Given a scientific problem, the student will identify and evaluate the outputs through appropriate visualisation. 4. Given a scientific problem, the student will discriminate and select basic approaches to scientific computing, including the use of cloud services. 5. Given an appropriate data set, the student will write a program to process the data e.g. find the principal components. 6. Given an image, the student will write a program to implement an operation on the image e.g. dithering to reduce its bit depth. 7. Given a classifier, the student will write a program to implement and analyse it e.g. plot its decision boundary; display an animation of its trajectory of weights over the error surface. Affective (Attitudes and Values) 1. Given datasets, the student will question whether the data is representative and attempt to address any biases 2. Given problems to investigate, the student will identify and discuss any potential ethical considerations. 3. On completion of an investigation using appropriate outputs including visualisation, the student will be able to defend the approach adopted. Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: The module will be delivered using a blended learning approach using on-line lectures, labs and tutorials. Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Langtangen (2016) A Primer on Scientific Programming with Python, Springer Other Texts: Beazley (2016) Python Essential Reference, 4th ed., O'Reilly Flach (2012) Machine Learning, Cambridge Goodfellow & Bengio (2014) Deep Learning, MIT Press Marsland (2014) Machine Learning: An Algorithmic Perspective, CRC Press Foster & Gannon (2017) Cloud Computing for Science and Engineering, MIT Press Programmes Semester(s) Module is Offered: Autumn Module Leader: [email protected]",
"question": "What will I learn about datasets in the scientific computing for ai module?",
"answers": [
{
"text": "question whether the data is representative and attempt to address any biases",
"answer_start": 1148
}
]
},
{
"id": "33",
"title": "INTRODUCTION_TO_SCIENTIFIC_COMPUTING_FOR_AI",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) 1. Given a target programming language, the student will become proficient in the syntax necessary to implement standard programming constructs. 2. Given a set of basic scientific problems, the student will construct simple programmes to investigate the problems. 3. Given a scientific problem, the student will identify and evaluate the outputs through appropriate visualisation. 4. Given a scientific problem, the student will discriminate and select basic approaches to scientific computing, including the use of cloud services. 5. Given an appropriate data set, the student will write a program to process the data e.g. find the principal components. 6. Given an image, the student will write a program to implement an operation on the image e.g. dithering to reduce its bit depth. 7. Given a classifier, the student will write a program to implement and analyse it e.g. plot its decision boundary; display an animation of its trajectory of weights over the error surface. Affective (Attitudes and Values) 1. Given datasets, the student will question whether the data is representative and attempt to address any biases 2. Given problems to investigate, the student will identify and discuss any potential ethical considerations. 3. On completion of an investigation using appropriate outputs including visualisation, the student will be able to defend the approach adopted. Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: The module will be delivered using a blended learning approach using on-line lectures, labs and tutorials. Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Langtangen (2016) A Primer on Scientific Programming with Python, Springer Other Texts: Beazley (2016) Python Essential Reference, 4th ed., O'Reilly Flach (2012) Machine Learning, Cambridge Goodfellow & Bengio (2014) Deep Learning, MIT Press Marsland (2014) Machine Learning: An Algorithmic Perspective, CRC Press Foster & Gannon (2017) Cloud Computing for Science and Engineering, MIT Press Programmes Semester(s) Module is Offered: Autumn Module Leader: [email protected]",
"question": "What textbook I need for the scientific computing for ai module?",
"answers": [
{
"text": "A Primer on Scientific Programming with Python",
"answer_start": 1803
}
]
},
{
"id": "34",
"title": "INTRODUCTION_TO_DEEP_LEARNING_AND_FRAMEWORKS",
"context": "Module Code - Title: CE4031 - INTRODUCTION TO DEEP LEARNING AND FRAMEWORKS Prerequisite Modules: Rationale and Purpose of the Module: To give students an insight into Deep Learning and associated Frameworks and prepare them to take more advanced Artificial Intelligence modules. Syllabus: 1. Fundamentals and basic concepts of deep learning and related machine learning 2. Programming basics for deep learning 3. Introduction to deep learning frameworks (e.g. TensorFlow, PyTorch, Caffe2, CNTK etc. ) 4. Deep learning platforms and acceleration 5. Applications of deep learning (e.g. image classification, signal processing, natural language processing etc)",
"question": "What is the code for the introduction to deep learning and frameworks module?",
"answers": [
{
"text": "CE4031",
"answer_start": 21
}
]
},
{
"id": "35",
"title": "INTRODUCTION_TO_DEEP_LEARNING_AND_FRAMEWORKS",
"context": "Module Code - Title: CE4031 - INTRODUCTION TO DEEP LEARNING AND FRAMEWORKS Prerequisite Modules: Rationale and Purpose of the Module: To give students an insight into Deep Learning and associated Frameworks and prepare them to take more advanced Artificial Intelligence modules. Syllabus: 1. Fundamentals and basic concepts of deep learning and related machine learning 2. Programming basics for deep learning 3. Introduction to deep learning frameworks (e.g. TensorFlow, PyTorch, Caffe2, CNTK etc. ) 4. Deep learning platforms and acceleration 5. Applications of deep learning (e.g. image classification, signal processing, natural language processing etc)",
"question": "What deep learning frameworks will I use in the introduction to deep learning and frameworks module?",
"answers": [
{
"text": "TensorFlow, PyTorch, Caffe2, CNTK",
"answer_start": 463
}
]
},
{
"id": "36",
"title": "INTRODUCTION_TO_DEEP_LEARNING_AND_FRAMEWORKS",
"context": "Module Code - Title: CE4031 - INTRODUCTION TO DEEP LEARNING AND FRAMEWORKS Prerequisite Modules: Rationale and Purpose of the Module: To give students an insight into Deep Learning and associated Frameworks and prepare them to take more advanced Artificial Intelligence modules. Syllabus: 1. Fundamentals and basic concepts of deep learning and related machine learning 2. Programming basics for deep learning 3. Introduction to deep learning frameworks (e.g. TensorFlow, PyTorch, Caffe2, CNTK etc. ) 4. Deep learning platforms and acceleration 5. Applications of deep learning (e.g. image classification, signal processing, natural language processing etc)",
"question": "What applications of deep learning will I learn about in the introduction to deep learning and frameworks module?",
"answers": [
{
"text": "image classification, signal processing, natural language processing",
"answer_start": 587
}
]
},
{
"id": "37",
"title": "INTRODUCTION_TO_DEEP_LEARNING_AND_FRAMEWORKS",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) 1. Given information and instruction, the student will gain insight and understand the key components in machine learning and deep learning systems. 2. Given representative problems, the student will be able to identify use-cases for machine learning and deep learning 3. Given information on prominent deep learning frameworks, the student will understand and compare their core features and usability. 4. Given a relevant cloud hosted platform, the student will develop the ability to use the supported deep learning frameworks. 5. Given problems to investigate, the student will implement, analyse and present outputs from deep learning frameworks. 6. Given large and real-world data sets for deep neural networks, the student will develop the ability to process and analyse the data. 7. Given selected practical problems, the student will have the ability to identify, develop and implement appropriate deep learning solutions. Affective (Attitudes and Values) 1. Given problems and data to investigate, the student will identify and discuss any significant ethical issues such as privacy, confidentiality, ownership, transparency and identity. 2. Given datasets, the student will question and demonstrate whether the data is representative and identify potential biases. 3. Following exposure to various frameworks and hosted platforms, the student will judge and challenge the limitations of current deep learning techniques. Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: The module will be delivered using a blended learning approach using on-line lectures, labs and tutorials. Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Goodfellow & Bengio (2016) Deep Learning, MIT Press Other Texts: Chollet (2017) Deep Learning with Python, Manning Publications Kim (2017) MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence, Apress Subramanian (2017) Deep Leaning with PyTorch, Packt Publishing Langtangen (2016) A Primer on Scientific Programming with Python, Springer Beazley (2009) Python Essential Reference, O'Reilly Marsland (2014) Machine Learning: An Algorithmic Perspective, CRC Press Programmes Semester(s) Module is Offered: Autumn Module Leader: [email protected]",
"question": "What will I learn in the introduction to deep learning and frameworks module?",
"answers": [
{
"text": "judge and challenge the limitations of current deep learning techniques",
"answer_start": 1461
}
]
},
{
"id": "38",
"title": "INTRODUCTION_TO_DEEP_LEARNING_AND_FRAMEWORKS",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) 1. Given information and instruction, the student will gain insight and understand the key components in machine learning and deep learning systems. 2. Given representative problems, the student will be able to identify use-cases for machine learning and deep learning 3. Given information on prominent deep learning frameworks, the student will understand and compare their core features and usability. 4. Given a relevant cloud hosted platform, the student will develop the ability to use the supported deep learning frameworks. 5. Given problems to investigate, the student will implement, analyse and present outputs from deep learning frameworks. 6. Given large and real-world data sets for deep neural networks, the student will develop the ability to process and analyse the data. 7. Given selected practical problems, the student will have the ability to identify, develop and implement appropriate deep learning solutions. Affective (Attitudes and Values) 1. Given problems and data to investigate, the student will identify and discuss any significant ethical issues such as privacy, confidentiality, ownership, transparency and identity. 2. Given datasets, the student will question and demonstrate whether the data is representative and identify potential biases. 3. Following exposure to various frameworks and hosted platforms, the student will judge and challenge the limitations of current deep learning techniques. Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: The module will be delivered using a blended learning approach using on-line lectures, labs and tutorials. Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Goodfellow & Bengio (2016) Deep Learning, MIT Press Other Texts: Chollet (2017) Deep Learning with Python, Manning Publications Kim (2017) MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence, Apress Subramanian (2017) Deep Leaning with PyTorch, Packt Publishing Langtangen (2016) A Primer on Scientific Programming with Python, Springer Beazley (2009) Python Essential Reference, O'Reilly Marsland (2014) Machine Learning: An Algorithmic Perspective, CRC Press Programmes Semester(s) Module is Offered: Autumn Module Leader: [email protected]",
"question": "How is the introduction to deep learning and frameworks module taught?",
"answers": [
{
"text": "on-line lectures, labs and tutorials",
"answer_start": 1722
}
]
},
{
"id": "39",
"title": "INTRODUCTION_TO_DEEP_LEARNING_AND_FRAMEWORKS",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) 1. Given information and instruction, the student will gain insight and understand the key components in machine learning and deep learning systems. 2. Given representative problems, the student will be able to identify use-cases for machine learning and deep learning 3. Given information on prominent deep learning frameworks, the student will understand and compare their core features and usability. 4. Given a relevant cloud hosted platform, the student will develop the ability to use the supported deep learning frameworks. 5. Given problems to investigate, the student will implement, analyse and present outputs from deep learning frameworks. 6. Given large and real-world data sets for deep neural networks, the student will develop the ability to process and analyse the data. 7. Given selected practical problems, the student will have the ability to identify, develop and implement appropriate deep learning solutions. Affective (Attitudes and Values) 1. Given problems and data to investigate, the student will identify and discuss any significant ethical issues such as privacy, confidentiality, ownership, transparency and identity. 2. Given datasets, the student will question and demonstrate whether the data is representative and identify potential biases. 3. Following exposure to various frameworks and hosted platforms, the student will judge and challenge the limitations of current deep learning techniques. Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: The module will be delivered using a blended learning approach using on-line lectures, labs and tutorials. Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Goodfellow & Bengio (2016) Deep Learning, MIT Press Other Texts: Chollet (2017) Deep Learning with Python, Manning Publications Kim (2017) MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence, Apress Subramanian (2017) Deep Leaning with PyTorch, Packt Publishing Langtangen (2016) A Primer on Scientific Programming with Python, Springer Beazley (2009) Python Essential Reference, O'Reilly Marsland (2014) Machine Learning: An Algorithmic Perspective, CRC Press Programmes Semester(s) Module is Offered: Autumn Module Leader: [email protected]",
"question": "In what semester the introduction to deep learning and frameworks module takes place?",
"answers": [
{
"text": "Autumn",
"answer_start": 2378
}
]
},
{
"id": "40",
"title": "ARTIFICIAL_INTELLIGENCE",
"context": "Module Code - Title: CE4041 - ARTIFICIAL INTELLIGENCE Prerequisite Modules: CE4703 Rationale and Purpose of the Module: To provide the student with a solid theoretical and practical understanding, knowledge and skill in the application of artificial intelligence and expert systems. This new module is created to provide an appropriate BE/ME masters level code to the level 9 module in AI in ECE department. This module will be offered to the Master of Engineering in Electronic and Computer Engineering programme using module ID 3301 Artificial Intelligence Syllabus: Section (i) - Introduction to Prolog and Logic Programming Rule-based systems and logic programming. The resolution principle, unification & backtracking. Recursion & iteration. Prolog representation of algorithms. Extra-logical features of Prolog. Section (ii) - State-Space Search Use of state-space search in A.I. programming. Representation of problems in state-space form. Prolog representation of state-spaces. Heuristics. Search strategies: depth-first, breadth-first, hillclimbing, best-first, branch & bound, Algorithm A, Algorithm A*. Admissibility, Monotonicity, Informedness. Section (iii) - Expert Systems The structure of an expert system. Knowledge Representation. The inference engine. Inference strategies. Reasoning under uncertainty. Section (iv) - Neural Networks Neural models: McCulloch & Pitts, Rosenblatt. Hebbian learning. The Adaline. Multi-layer Perceptrons & Backpropagation. Associative networks. Competitive networks.",
"question": "What is the code for the artificial intelligence module?",
"answers": [
{
"text": "CE4041",
"answer_start": 21
}
]
},
{
"id": "41",
"title": "ARTIFICIAL_INTELLIGENCE",
"context": "Module Code - Title: CE4041 - ARTIFICIAL INTELLIGENCE Prerequisite Modules: CE4703 Rationale and Purpose of the Module: To provide the student with a solid theoretical and practical understanding, knowledge and skill in the application of artificial intelligence and expert systems. This new module is created to provide an appropriate BE/ME masters level code to the level 9 module in AI in ECE department. This module will be offered to the Master of Engineering in Electronic and Computer Engineering programme using module ID 3301 Artificial Intelligence Syllabus: Section (i) - Introduction to Prolog and Logic Programming Rule-based systems and logic programming. The resolution principle, unification & backtracking. Recursion & iteration. Prolog representation of algorithms. Extra-logical features of Prolog. Section (ii) - State-Space Search Use of state-space search in A.I. programming. Representation of problems in state-space form. Prolog representation of state-spaces. Heuristics. Search strategies: depth-first, breadth-first, hillclimbing, best-first, branch & bound, Algorithm A, Algorithm A*. Admissibility, Monotonicity, Informedness. Section (iii) - Expert Systems The structure of an expert system. Knowledge Representation. The inference engine. Inference strategies. Reasoning under uncertainty. Section (iv) - Neural Networks Neural models: McCulloch & Pitts, Rosenblatt. Hebbian learning. The Adaline. Multi-layer Perceptrons & Backpropagation. Associative networks. Competitive networks.",
"question": "Is there a prerequisite module for the artificial intelligence module?",
"answers": [
{
"text": "CE4703",
"answer_start": 76
}
]
},
{
"id": "42",
"title": "ARTIFICIAL_INTELLIGENCE",
"context": "Module Code - Title: CE4041 - ARTIFICIAL INTELLIGENCE Prerequisite Modules: CE4703 Rationale and Purpose of the Module: To provide the student with a solid theoretical and practical understanding, knowledge and skill in the application of artificial intelligence and expert systems. This new module is created to provide an appropriate BE/ME masters level code to the level 9 module in AI in ECE department. This module will be offered to the Master of Engineering in Electronic and Computer Engineering programme using module ID 3301 Artificial Intelligence Syllabus: Section (i) - Introduction to Prolog and Logic Programming Rule-based systems and logic programming. The resolution principle, unification & backtracking. Recursion & iteration. Prolog representation of algorithms. Extra-logical features of Prolog. Section (ii) - State-Space Search Use of state-space search in A.I. programming. Representation of problems in state-space form. Prolog representation of state-spaces. Heuristics. Search strategies: depth-first, breadth-first, hillclimbing, best-first, branch & bound, Algorithm A, Algorithm A*. Admissibility, Monotonicity, Informedness. Section (iii) - Expert Systems The structure of an expert system. Knowledge Representation. The inference engine. Inference strategies. Reasoning under uncertainty. Section (iv) - Neural Networks Neural models: McCulloch & Pitts, Rosenblatt. Hebbian learning. The Adaline. Multi-layer Perceptrons & Backpropagation. Associative networks. Competitive networks.",
"question": "What are some of the search algorithms that I will learn about in the artificial intelligence module?",
"answers": [
{
"text": "Algorithm A, Algorithm A*.",
"answer_start": 1088
}
]
},
{
"id": "43",
"title": "ARTIFICIAL_INTELLIGENCE",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) 1. Use the resolution technique to solve problems stated in terms of predicate logic. 2. Formulate a search problem in terms of an appropriate state-space representation. 3. Apply suitable search algorithms and heuristics to problem solving. 4. Apply neural network techniques to the solution of classification problems. 5. Construct problem-solving programs in a suitable A.I. language such as Lisp or Prolog. 6. Evaluate the current state of the art in artificial intelligence research and applications. Affective (Attitudes and Values) Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Lectures/Labs/Tutorials, Self-directed research and project work. Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Luger, G.F. (2005) Artificial Intelligence, 5th ed., Pearson/Addison-Wesley Russell, S. & Norvig, P. (2003) Artificial Intelligence: A Modern Approach, 2nd ed., Pearson/Addison-Wesley Other Texts: Bishop, C.M. (2006) Pattern Recognition & machine Learning, Springer Levesque, H.J. (2004) Brachman, R.J. & Knowledge Representation & Reasoning., Elsevier Alpaydin, E. (2003) Introduction to Machine Learning, MIT Press McKay, D. (2003) 2003 Information Theory, Inference & Learning Algorithms., Cambridge Dechter, R. (2003) Constraint Processing., Elsevier Negnevitsky, M. (2002) Artificial Intelligence: A Guide to Intelligent Systems, Pearson Bratko, I. (2000) Prolog Programming for Artificial Intelligence, 3rd ed. , Addison-Wesley Nilsson, N.J. (1998) Artificial Intelligence: A New Synthesis, Morgan Kaufmann Programmes BEECENUFA - ELECTRONIC AND COMPUTER ENGINEERING Semester(s) Module is Offered: Autumn Module Leader: [email protected]",
"question": "What programming languages will I learn about in the artificial intelligence module?",
"answers": [
{
"text": "Lisp or Prolog",
"answer_start": 497
}
]
},
{
"id": "44",
"title": "ARTIFICIAL_INTELLIGENCE",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) 1. Use the resolution technique to solve problems stated in terms of predicate logic. 2. Formulate a search problem in terms of an appropriate state-space representation. 3. Apply suitable search algorithms and heuristics to problem solving. 4. Apply neural network techniques to the solution of classification problems. 5. Construct problem-solving programs in a suitable A.I. language such as Lisp or Prolog. 6. Evaluate the current state of the art in artificial intelligence research and applications. Affective (Attitudes and Values) Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Lectures/Labs/Tutorials, Self-directed research and project work. Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Luger, G.F. (2005) Artificial Intelligence, 5th ed., Pearson/Addison-Wesley Russell, S. & Norvig, P. (2003) Artificial Intelligence: A Modern Approach, 2nd ed., Pearson/Addison-Wesley Other Texts: Bishop, C.M. (2006) Pattern Recognition & machine Learning, Springer Levesque, H.J. (2004) Brachman, R.J. & Knowledge Representation & Reasoning., Elsevier Alpaydin, E. (2003) Introduction to Machine Learning, MIT Press McKay, D. (2003) 2003 Information Theory, Inference & Learning Algorithms., Cambridge Dechter, R. (2003) Constraint Processing., Elsevier Negnevitsky, M. (2002) Artificial Intelligence: A Guide to Intelligent Systems, Pearson Bratko, I. (2000) Prolog Programming for Artificial Intelligence, 3rd ed. , Addison-Wesley Nilsson, N.J. (1998) Artificial Intelligence: A New Synthesis, Morgan Kaufmann Programmes BEECENUFA - ELECTRONIC AND COMPUTER ENGINEERING Semester(s) Module is Offered: Autumn Module Leader: [email protected]",
"question": "What are the contact details of the lecturer for the artificial intelligence module?",
"answers": [
{
"text": "[email protected]",
"answer_start": 1833
}
]
},
{
"id": "45",
"title": "ARTIFICIAL_INTELLIGENCE",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) 1. Use the resolution technique to solve problems stated in terms of predicate logic. 2. Formulate a search problem in terms of an appropriate state-space representation. 3. Apply suitable search algorithms and heuristics to problem solving. 4. Apply neural network techniques to the solution of classification problems. 5. Construct problem-solving programs in a suitable A.I. language such as Lisp or Prolog. 6. Evaluate the current state of the art in artificial intelligence research and applications. Affective (Attitudes and Values) Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Lectures/Labs/Tutorials, Self-directed research and project work. Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Luger, G.F. (2005) Artificial Intelligence, 5th ed., Pearson/Addison-Wesley Russell, S. & Norvig, P. (2003) Artificial Intelligence: A Modern Approach, 2nd ed., Pearson/Addison-Wesley Other Texts: Bishop, C.M. (2006) Pattern Recognition & machine Learning, Springer Levesque, H.J. (2004) Brachman, R.J. & Knowledge Representation & Reasoning., Elsevier Alpaydin, E. (2003) Introduction to Machine Learning, MIT Press McKay, D. (2003) 2003 Information Theory, Inference & Learning Algorithms., Cambridge Dechter, R. (2003) Constraint Processing., Elsevier Negnevitsky, M. (2002) Artificial Intelligence: A Guide to Intelligent Systems, Pearson Bratko, I. (2000) Prolog Programming for Artificial Intelligence, 3rd ed. , Addison-Wesley Nilsson, N.J. (1998) Artificial Intelligence: A New Synthesis, Morgan Kaufmann Programmes BEECENUFA - ELECTRONIC AND COMPUTER ENGINEERING Semester(s) Module is Offered: Autumn Module Leader: [email protected]",
"question": "Who is the lecturer for the artificial intelligence module?",
"answers": [
{
"text": "Colin.Flanagan",
"answer_start": 1833
}
]
},
{
"id": "46",
"title": "INTRODUCTION_TO_DATA_ENGINEERING_AND_MACHINE_LEARNING",
"context": "Module Code - Title: CE4051 - INTRODUCTION TO DATA ENGINEERING AND MACHINE LEARNING Prerequisite Modules: Rationale and Purpose of the Module: To give students an insight and grounding into data engineering and machine learning and prepare them to take more advanced Artificial Intelligence modules. The module will cover mathematical and coding skills essential to developing machine learning applications in Python and will provide an introduction to more advanced machine learning topics such as modern machine learning platforms, data visualisation and deep learning. Syllabus: Students undertaking this module will undertake learning in: a programming language (e.g. Python) for machine learning; numeric support in typical scientific scripting (e.g., Numpy/Scipy); graphics and Scientific Visualization: Using scripting languages to build scientific visualizations (Matplotlib); fundamentals and basic concepts of machine learning algorithms (Perceptron, Logistic Regression, Support Vector Machines, Multi-Layer Perceptron); programming basics for machine learning (Scikitlearn, Pandas); and, applications of machine learning (e.g. inference, image classification, etc)",
"question": "What is the code for the introduction to data engineering and machine learning module?",
"answers": [
{
"text": "CE4051",
"answer_start": 21
}
]
},
{
"id": "47",
"title": "INTRODUCTION_TO_DATA_ENGINEERING_AND_MACHINE_LEARNING",
"context": "Module Code - Title: CE4051 - INTRODUCTION TO DATA ENGINEERING AND MACHINE LEARNING Prerequisite Modules: Rationale and Purpose of the Module: To give students an insight and grounding into data engineering and machine learning and prepare them to take more advanced Artificial Intelligence modules. The module will cover mathematical and coding skills essential to developing machine learning applications in Python and will provide an introduction to more advanced machine learning topics such as modern machine learning platforms, data visualisation and deep learning. Syllabus: Students undertaking this module will undertake learning in: a programming language (e.g. Python) for machine learning; numeric support in typical scientific scripting (e.g., Numpy/Scipy); graphics and Scientific Visualization: Using scripting languages to build scientific visualizations (Matplotlib); fundamentals and basic concepts of machine learning algorithms (Perceptron, Logistic Regression, Support Vector Machines, Multi-Layer Perceptron); programming basics for machine learning (Scikitlearn, Pandas); and, applications of machine learning (e.g. inference, image classification, etc)",
"question": "What machine learning algorithms will I learn about in the introduction to data engineering and machine learning module?",
"answers": [
{
"text": "Perceptron, Logistic Regression, Support Vector Machines",
"answer_start": 949
}
]
},
{
"id": "48",
"title": "INTRODUCTION_TO_DATA_ENGINEERING_AND_MACHINE_LEARNING",
"context": "Module Code - Title: CE4051 - INTRODUCTION TO DATA ENGINEERING AND MACHINE LEARNING Prerequisite Modules: Rationale and Purpose of the Module: To give students an insight and grounding into data engineering and machine learning and prepare them to take more advanced Artificial Intelligence modules. The module will cover mathematical and coding skills essential to developing machine learning applications in Python and will provide an introduction to more advanced machine learning topics such as modern machine learning platforms, data visualisation and deep learning. Syllabus: Students undertaking this module will undertake learning in: a programming language (e.g. Python) for machine learning; numeric support in typical scientific scripting (e.g., Numpy/Scipy); graphics and Scientific Visualization: Using scripting languages to build scientific visualizations (Matplotlib); fundamentals and basic concepts of machine learning algorithms (Perceptron, Logistic Regression, Support Vector Machines, Multi-Layer Perceptron); programming basics for machine learning (Scikitlearn, Pandas); and, applications of machine learning (e.g. inference, image classification, etc)",
"question": "Will there be mathematics in the introduction to data engineering and machine learning module?",
"answers": [
{
"text": "The module will cover mathematical and coding skills",
"answer_start": 300
}
]
},
{
"id": "49",
"title": "INTRODUCTION_TO_DATA_ENGINEERING_AND_MACHINE_LEARNING",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) On successful completion of this module, students will be able to: 1. Understand the key components of machine learning systems. 2. Justify the use of appropriate machine learning approaches for given applications. 3. Apply suitable visualisation, pre-, and post-processing technique. 4. Investigate trends and potential biases in data pertaining to machine learning problems. Affective (Attitudes and Values) On successful completion of this module, students will be able to: 1. Defend the machine learning approach adopted in solving given problems. 2. Understand that there is no single machine learner that is best in all cases (the so-called 'No Free Lunch Theorem'). Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: The module will be delivered using a blended learning approach with online aspects as well as face-to-face interaction. The content is divided into two-week activities with a submission at the end of every two-week window. Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Sebastian Raschka & Vahid Mirhjalili (2017) Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition, Packt Publishing Introduction to Machine Learning with Python: A Guide for Data Scientists (2016) Andreas C. M眉ller and Sarah Guido, O'Reilly Erwin Kreyszig (2006) ADVANCED ENGINEERING MATHEMATICS, Wiley Other Texts: Brian K. Jones and David M. Beazley (2011) Python Cookbook: Recipes for Mastering Python 3, O'Reilly Programmes MSAIMLTFA - MS ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Semester(s) Module is Offered: Autumn Module Leader: [email protected]",
"question": "What will I learn in the data engineering and machine learning module?",
"answers": [
{
"text": "Understand the key components of machine learning systems",
"answer_start": 172
}
]
},
{
"id": "50",
"title": "INTRODUCTION_TO_DATA_ENGINEERING_AND_MACHINE_LEARNING",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) On successful completion of this module, students will be able to: 1. Understand the key components of machine learning systems. 2. Justify the use of appropriate machine learning approaches for given applications. 3. Apply suitable visualisation, pre-, and post-processing technique. 4. Investigate trends and potential biases in data pertaining to machine learning problems. Affective (Attitudes and Values) On successful completion of this module, students will be able to: 1. Defend the machine learning approach adopted in solving given problems. 2. Understand that there is no single machine learner that is best in all cases (the so-called 'No Free Lunch Theorem'). Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: The module will be delivered using a blended learning approach with online aspects as well as face-to-face interaction. The content is divided into two-week activities with a submission at the end of every two-week window. Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Sebastian Raschka & Vahid Mirhjalili (2017) Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition, Packt Publishing Introduction to Machine Learning with Python: A Guide for Data Scientists (2016) Andreas C. M眉ller and Sarah Guido, O'Reilly Erwin Kreyszig (2006) ADVANCED ENGINEERING MATHEMATICS, Wiley Other Texts: Brian K. Jones and David M. Beazley (2011) Python Cookbook: Recipes for Mastering Python 3, O'Reilly Programmes MSAIMLTFA - MS ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Semester(s) Module is Offered: Autumn Module Leader: [email protected]",
"question": "How will the data engineering and machine learning module be delivered?",
"answers": [
{
"text": "using a blended learning approach",
"answer_start": 924
}
]
},
{
"id": "51",
"title": "INTRODUCTION_TO_DATA_ENGINEERING_AND_MACHINE_LEARNING",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) On successful completion of this module, students will be able to: 1. Understand the key components of machine learning systems. 2. Justify the use of appropriate machine learning approaches for given applications. 3. Apply suitable visualisation, pre-, and post-processing technique. 4. Investigate trends and potential biases in data pertaining to machine learning problems. Affective (Attitudes and Values) On successful completion of this module, students will be able to: 1. Defend the machine learning approach adopted in solving given problems. 2. Understand that there is no single machine learner that is best in all cases (the so-called 'No Free Lunch Theorem'). Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: The module will be delivered using a blended learning approach with online aspects as well as face-to-face interaction. The content is divided into two-week activities with a submission at the end of every two-week window. Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Sebastian Raschka & Vahid Mirhjalili (2017) Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition, Packt Publishing Introduction to Machine Learning with Python: A Guide for Data Scientists (2016) Andreas C. M眉ller and Sarah Guido, O'Reilly Erwin Kreyszig (2006) ADVANCED ENGINEERING MATHEMATICS, Wiley Other Texts: Brian K. Jones and David M. Beazley (2011) Python Cookbook: Recipes for Mastering Python 3, O'Reilly Programmes MSAIMLTFA - MS ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Semester(s) Module is Offered: Autumn Module Leader: [email protected]",
"question": "When is the data engineering and machine learning module offered?",
"answers": [
{
"text": "Autumn",
"answer_start": 1776
}
]
},
{
"id": "52",
"title": "OPERATING_SYSTEMS_1",
"context": "Module Code - Title: CE4204 - OPERATING SYSTEMS 1 Prerequisite Modules: EE4513 CE4702 Rationale and Purpose of the Module: To introduce a complete single-user, disk based operating system. Students will already understand small systems at the logic level and at the programmer脝s model level. The module will include a project incorporating the design/use of an operating system tool. Syllabus: Operating system definitions, components, command shells, services overview. Review of 80x86 assembly language programming techniques. Memory map organisation, Extended and Expanded memory. Process execution. Interrupt handlers, BIOS and DOS functions. Device drivers and Resident Utilities: Data structures used in operating system design. Disk Storage Organisation:. Introduction to Microsoft Windows XP.",
"question": "What is the code for the operating systems 1 module?",
"answers": [
{
"text": "CE4204",
"answer_start": 21
}
]
},
{
"id": "53",
"title": "OPERATING_SYSTEMS_1",
"context": "Module Code - Title: CE4204 - OPERATING SYSTEMS 1 Prerequisite Modules: EE4513 CE4702 Rationale and Purpose of the Module: To introduce a complete single-user, disk based operating system. Students will already understand small systems at the logic level and at the programmer脝s model level. The module will include a project incorporating the design/use of an operating system tool. Syllabus: Operating system definitions, components, command shells, services overview. Review of 80x86 assembly language programming techniques. Memory map organisation, Extended and Expanded memory. Process execution. Interrupt handlers, BIOS and DOS functions. Device drivers and Resident Utilities: Data structures used in operating system design. Disk Storage Organisation:. Introduction to Microsoft Windows XP.",
"question": "Are there any prerequisite modules for the operating systems 1 module?",
"answers": [
{
"text": "EE4513 CE4702",
"answer_start": 72
}
]
},
{
"id": "54",
"title": "OPERATING_SYSTEMS_1",
"context": "Module Code - Title: CE4204 - OPERATING SYSTEMS 1 Prerequisite Modules: EE4513 CE4702 Rationale and Purpose of the Module: To introduce a complete single-user, disk based operating system. Students will already understand small systems at the logic level and at the programmer脝s model level. The module will include a project incorporating the design/use of an operating system tool. Syllabus: Operating system definitions, components, command shells, services overview. Review of 80x86 assembly language programming techniques. Memory map organisation, Extended and Expanded memory. Process execution. Interrupt handlers, BIOS and DOS functions. Device drivers and Resident Utilities: Data structures used in operating system design. Disk Storage Organisation:. Introduction to Microsoft Windows XP.",
"question": "What will I learn in the operating systems 1 module?",
"answers": [
{
"text": "small systems at the logic level and at the programmers model level",
"answer_start": -1
}
]
},
{
"id": "55",
"title": "OPERATING_SYSTEMS_1",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) 1. Recall and describe the components and services provided by a single task operating system. 2. Describe and illustrate how memory management and memory addressing is performed in a single task operating system. 3. Identify and describe the data structures used in single task operating system design. 4. Recall and explain how files are organised and tracked on a FAT disk partition. 5. Describe and demonstrate with regards to TSR program design the operation and use of hardware interrupts 8 (clock) and 9 (keyboard). 6. Design, implement and demonstrate a working operating system component for a single task operating system such as a device driver, TSR or disk utility. Affective (Attitudes and Values) Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Lectures, Labs and Project work. Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Tanenbaum, A.S. (2001) Modern Operating Systems. 2nd Edition., Prentice Hall Davis and Rajkumar (2001) Operating Systems: A systematic view. 5th Edition., Addison Wesley. Other Texts: Nutt, G., (2004) Operating Systems. 3rd Edition., Addison Wesley. Deitel, Deitel and Choffnes (2004) Operating Systems. 3rd Edition., Prentice Hall Programmes Semester(s) Module is Offered: Module Leader: [email protected]",
"question": "What are the prime texts for the operating systems 1 module?",
"answers": [
{
"text": "Tanenbaum, A.S. (2001) Modern Operating Systems",
"answer_start": 1044
}
]
},
{
"id": "56",
"title": "OPERATING_SYSTEMS_1",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) 1. Recall and describe the components and services provided by a single task operating system. 2. Describe and illustrate how memory management and memory addressing is performed in a single task operating system. 3. Identify and describe the data structures used in single task operating system design. 4. Recall and explain how files are organised and tracked on a FAT disk partition. 5. Describe and demonstrate with regards to TSR program design the operation and use of hardware interrupts 8 (clock) and 9 (keyboard). 6. Design, implement and demonstrate a working operating system component for a single task operating system such as a device driver, TSR or disk utility. Affective (Attitudes and Values) Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Lectures, Labs and Project work. Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Tanenbaum, A.S. (2001) Modern Operating Systems. 2nd Edition., Prentice Hall Davis and Rajkumar (2001) Operating Systems: A systematic view. 5th Edition., Addison Wesley. Other Texts: Nutt, G., (2004) Operating Systems. 3rd Edition., Addison Wesley. Deitel, Deitel and Choffnes (2004) Operating Systems. 3rd Edition., Prentice Hall Programmes Semester(s) Module is Offered: Module Leader: [email protected]",
"question": "What will I learn about files in the operating systems 1 module?",
"answers": [
{
"text": "Recall and explain how files are organised and tracked on a FAT disk partition",
"answer_start": 410
}
]
},
{
"id": "57",
"title": "OPERATING_SYSTEMS_1",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) 1. Recall and describe the components and services provided by a single task operating system. 2. Describe and illustrate how memory management and memory addressing is performed in a single task operating system. 3. Identify and describe the data structures used in single task operating system design. 4. Recall and explain how files are organised and tracked on a FAT disk partition. 5. Describe and demonstrate with regards to TSR program design the operation and use of hardware interrupts 8 (clock) and 9 (keyboard). 6. Design, implement and demonstrate a working operating system component for a single task operating system such as a device driver, TSR or disk utility. Affective (Attitudes and Values) Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Lectures, Labs and Project work. Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Tanenbaum, A.S. (2001) Modern Operating Systems. 2nd Edition., Prentice Hall Davis and Rajkumar (2001) Operating Systems: A systematic view. 5th Edition., Addison Wesley. Other Texts: Nutt, G., (2004) Operating Systems. 3rd Edition., Addison Wesley. Deitel, Deitel and Choffnes (2004) Operating Systems. 3rd Edition., Prentice Hall Programmes Semester(s) Module is Offered: Module Leader: [email protected]",
"question": "How can I contact the lecturer of the operating systems 1 module?",
"answers": [
{
"text": "[email protected]",
"answer_start": 1433
}
]
},
{
"id": "58",
"title": "MICROCOMPUTER_SYSTEMS",
"context": "Module Code - Title: CE4205 - MICROCOMPUTER SYSTEMS Prerequisite Modules: Rationale and Purpose of the Module: This module is designed for 'transferee students'. Students must be capable of writing programs at assembly language level for some modern computer or microprocessor. The main purpose is to: 1. Teach 8086 assembly language programming. 2. To introduce operating system design and implementation concepts based on a complete single-user, disk based operating system. MS-DOS and Microsoft Windows will be the example operating systems. Syllabus: 8086 assembly language programming. 8086 architecture, standard PC components, instruction set, linking, debugging. Operating system introduction. MS-DOS memory organisation. Interrupt handlers. Process execution, device drivers, disk storage organisation. Introduction to Microsoft Windows OS .",
"question": "What is the code for the microcomputer systems module?",
"answers": [
{
"text": "CE4205",
"answer_start": 21
}
]
},
{
"id": "59",
"title": "MICROCOMPUTER_SYSTEMS",
"context": "Module Code - Title: CE4205 - MICROCOMPUTER SYSTEMS Prerequisite Modules: Rationale and Purpose of the Module: This module is designed for 'transferee students'. Students must be capable of writing programs at assembly language level for some modern computer or microprocessor. The main purpose is to: 1. Teach 8086 assembly language programming. 2. To introduce operating system design and implementation concepts based on a complete single-user, disk based operating system. MS-DOS and Microsoft Windows will be the example operating systems. Syllabus: 8086 assembly language programming. 8086 architecture, standard PC components, instruction set, linking, debugging. Operating system introduction. MS-DOS memory organisation. Interrupt handlers. Process execution, device drivers, disk storage organisation. Introduction to Microsoft Windows OS .",
"question": "What programming language will I learn about in the microcomputer systems module?",
"answers": [
{
"text": "8086",
"answer_start": 311
}
]
},
{
"id": "60",
"title": "MICROCOMPUTER_SYSTEMS",
"context": "Module Code - Title: CE4205 - MICROCOMPUTER SYSTEMS Prerequisite Modules: Rationale and Purpose of the Module: This module is designed for 'transferee students'. Students must be capable of writing programs at assembly language level for some modern computer or microprocessor. The main purpose is to: 1. Teach 8086 assembly language programming. 2. To introduce operating system design and implementation concepts based on a complete single-user, disk based operating system. MS-DOS and Microsoft Windows will be the example operating systems. Syllabus: 8086 assembly language programming. 8086 architecture, standard PC components, instruction set, linking, debugging. Operating system introduction. MS-DOS memory organisation. Interrupt handlers. Process execution, device drivers, disk storage organisation. Introduction to Microsoft Windows OS .",
"question": "What will I learn in the microcomputer systems module?",
"answers": [
{
"text": "8086 assembly language programming",
"answer_start": 311
}
]
},
{
"id": "61",
"title": "MICROCOMPUTER_SYSTEMS",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) 1. Students will be able to define the 8086 architecture and recognise the importance of each system 2. Students will be able to define the key concepts in an Operating System 3. Students will be able to name and discuss various Operating Systems 4. Students will be able to explain the concepts of Disk Storage Organisation Affective (Attitudes and Values) No learning outcomes of this type in the module. Psychomotor (Physical Skills) 5. Students will be able to demonstrate skills in 8086 assembly language by designing and testing code 6. Students will be able to analyse and assess modern embedded system devices How the Module will be Taught and what will be the Learning Experiences of the Students: The module is based on the standard academic term structure, with 2 lecture hours and 2 laboratory hours per week. The module contains a significant software project which is worth a substantial portion of the module assessment (with the remaining amount assigned to the end of module written examination).Research Findings Incorporated in to the Syllabus (If Relevant):The module lecturer (Dr Derek O'Keeffe) is an active researcher in the area of embedded systems engineering. As such, the examples and projects are based on current industry needs and the types of embedded systems encountered in practice. Prime Texts: Barry B. Brey (2006) INTEL Microprocessors 8086/8088, 80186/80188, 80286, 80386, 80486, Pentium, Prentium ProProcessor, Pentium II, III, 4, Prentice Hall Other Texts: Kip Irvine (2007) Assembly Language for Intel-Based Computers, Prentice Hall Programmes Semester(s) Module is Offered: Module Leader: [email protected]",
"question": "How the microcomputer systems module is taught?",
"answers": [
{
"text": "2 lecture hours and 2 laboratory hours per week",
"answer_start": 875
}
]
},
{
"id": "62",
"title": "MICROCOMPUTER_SYSTEMS",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) 1. Students will be able to define the 8086 architecture and recognise the importance of each system 2. Students will be able to define the key concepts in an Operating System 3. Students will be able to name and discuss various Operating Systems 4. Students will be able to explain the concepts of Disk Storage Organisation Affective (Attitudes and Values) No learning outcomes of this type in the module. Psychomotor (Physical Skills) 5. Students will be able to demonstrate skills in 8086 assembly language by designing and testing code 6. Students will be able to analyse and assess modern embedded system devices How the Module will be Taught and what will be the Learning Experiences of the Students: The module is based on the standard academic term structure, with 2 lecture hours and 2 laboratory hours per week. The module contains a significant software project which is worth a substantial portion of the module assessment (with the remaining amount assigned to the end of module written examination).Research Findings Incorporated in to the Syllabus (If Relevant):The module lecturer (Dr Derek O'Keeffe) is an active researcher in the area of embedded systems engineering. As such, the examples and projects are based on current industry needs and the types of embedded systems encountered in practice. Prime Texts: Barry B. Brey (2006) INTEL Microprocessors 8086/8088, 80186/80188, 80286, 80386, 80486, Pentium, Prentium ProProcessor, Pentium II, III, 4, Prentice Hall Other Texts: Kip Irvine (2007) Assembly Language for Intel-Based Computers, Prentice Hall Programmes Semester(s) Module is Offered: Module Leader: [email protected]",
"question": "What concepts will I learn in the microcomputer systems module?",
"answers": [
{
"text": "Disk Storage Organisation Affective (Attitudes and Values)",
"answer_start": 401
}
]
},
{
"id": "63",
"title": "MICROCOMPUTER_SYSTEMS",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) 1. Students will be able to define the 8086 architecture and recognise the importance of each system 2. Students will be able to define the key concepts in an Operating System 3. Students will be able to name and discuss various Operating Systems 4. Students will be able to explain the concepts of Disk Storage Organisation Affective (Attitudes and Values) No learning outcomes of this type in the module. Psychomotor (Physical Skills) 5. Students will be able to demonstrate skills in 8086 assembly language by designing and testing code 6. Students will be able to analyse and assess modern embedded system devices How the Module will be Taught and what will be the Learning Experiences of the Students: The module is based on the standard academic term structure, with 2 lecture hours and 2 laboratory hours per week. The module contains a significant software project which is worth a substantial portion of the module assessment (with the remaining amount assigned to the end of module written examination).Research Findings Incorporated in to the Syllabus (If Relevant):The module lecturer (Dr Derek O'Keeffe) is an active researcher in the area of embedded systems engineering. As such, the examples and projects are based on current industry needs and the types of embedded systems encountered in practice. Prime Texts: Barry B. Brey (2006) INTEL Microprocessors 8086/8088, 80186/80188, 80286, 80386, 80486, Pentium, Prentium ProProcessor, Pentium II, III, 4, Prentice Hall Other Texts: Kip Irvine (2007) Assembly Language for Intel-Based Computers, Prentice Hall Programmes Semester(s) Module is Offered: Module Leader: [email protected]",
"question": "How can I contact the lecturer for the microcomputer systems module?",
"answers": [
{
"text": "[email protected]",
"answer_start": 1732
}
]
},
{
"id": "64",
"title": "OPERATING_SYSTEMS_2",
"context": "Module Code - Title: CE4206 - OPERATING SYSTEMS 2 Prerequisite Modules: CE4204 Rationale and Purpose of the Module: Study of multitasking operating systems. Study will be confined to single processor systems. A Unix or WIN-32 operating system will be selected as the prime example operating system. The module lab work will teach the student to develop concurrent program solutions. The module includes: concurrency, states, queues, scheduling. Process inter-communication. Memory management. File systems to support multitasking, File sharing, file protection, performance issues. Conditions for deadlock and solutions. I/O devices and device drivers. File security and protection.Syllabus: 1) Processes: Concurrency, states, queues, scheduling. 2) Process Communication: Mutual exclusion, race conditions, busy-waiting solutions, Test/Set locks, semaphores, monitors, simple message passing, pipes, classical problems. 3) Memory Management: Swapping, virtual memory, paging, segmentation, performance and protection issues. 4) File systems to support multitasking: File sharing, file protection, performance issues. The UNIX i-node system. 5) Deadlock: Conditions for deadlock and solutions. 6)Input/Output: I/O Devices for multitasking environments, need for design of re-entrant drivers. 7) Computer Security and Protection: User authentication; protection matrix; ACL; capabilities. 8) Case Study: The UNIX Operating System: Origins; Standards; Shells; Utilities; Process Management; Memory Management; File Management; Programming in the Unix environment (Or, equivalent study based on a WIN-32 operating system.)",
"question": "What is the code for the operating systems 2 module?",
"answers": [
{
"text": "CE4206",
"answer_start": 21
}
]
},
{
"id": "65",
"title": "OPERATING_SYSTEMS_2",
"context": "Module Code - Title: CE4206 - OPERATING SYSTEMS 2 Prerequisite Modules: CE4204 Rationale and Purpose of the Module: Study of multitasking operating systems. Study will be confined to single processor systems. A Unix or WIN-32 operating system will be selected as the prime example operating system. The module lab work will teach the student to develop concurrent program solutions. The module includes: concurrency, states, queues, scheduling. Process inter-communication. Memory management. File systems to support multitasking, File sharing, file protection, performance issues. Conditions for deadlock and solutions. I/O devices and device drivers. File security and protection.Syllabus: 1) Processes: Concurrency, states, queues, scheduling. 2) Process Communication: Mutual exclusion, race conditions, busy-waiting solutions, Test/Set locks, semaphores, monitors, simple message passing, pipes, classical problems. 3) Memory Management: Swapping, virtual memory, paging, segmentation, performance and protection issues. 4) File systems to support multitasking: File sharing, file protection, performance issues. The UNIX i-node system. 5) Deadlock: Conditions for deadlock and solutions. 6)Input/Output: I/O Devices for multitasking environments, need for design of re-entrant drivers. 7) Computer Security and Protection: User authentication; protection matrix; ACL; capabilities. 8) Case Study: The UNIX Operating System: Origins; Standards; Shells; Utilities; Process Management; Memory Management; File Management; Programming in the Unix environment (Or, equivalent study based on a WIN-32 operating system.)",
"question": "Are there any prerequisite modules for the operating systems 2 module?",
"answers": [
{
"text": "CE4204",
"answer_start": 73
}
]
},
{
"id": "66",
"title": "OPERATING_SYSTEMS_2",
"context": "Module Code - Title: CE4206 - OPERATING SYSTEMS 2 Prerequisite Modules: CE4204 Rationale and Purpose of the Module: Study of multitasking operating systems. Study will be confined to single processor systems. A Unix or WIN-32 operating system will be selected as the prime example operating system. The module lab work will teach the student to develop concurrent program solutions. The module includes: concurrency, states, queues, scheduling. Process inter-communication. Memory management. File systems to support multitasking, File sharing, file protection, performance issues. Conditions for deadlock and solutions. I/O devices and device drivers. File security and protection.Syllabus: 1) Processes: Concurrency, states, queues, scheduling. 2) Process Communication: Mutual exclusion, race conditions, busy-waiting solutions, Test/Set locks, semaphores, monitors, simple message passing, pipes, classical problems. 3) Memory Management: Swapping, virtual memory, paging, segmentation, performance and protection issues. 4) File systems to support multitasking: File sharing, file protection, performance issues. The UNIX i-node system. 5) Deadlock: Conditions for deadlock and solutions. 6)Input/Output: I/O Devices for multitasking environments, need for design of re-entrant drivers. 7) Computer Security and Protection: User authentication; protection matrix; ACL; capabilities. 8) Case Study: The UNIX Operating System: Origins; Standards; Shells; Utilities; Process Management; Memory Management; File Management; Programming in the Unix environment (Or, equivalent study based on a WIN-32 operating system.)",
"question": "What processes will I learn in the operating systems 2 module?",
"answers": [
{
"text": "concurrency, states, queues, scheduling",
"answer_start": 405
}
]
},
{
"id": "67",
"title": "OPERATING_SYSTEMS_2",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) - Be able to define the underlying concepts for computer operating system design. - Be able to identify concurrency problems in software examples and describe how they can be fixed using appropriate synchronisation mechanisms. -Compare the features of two separate operating systems (Unix and WIN-32) by identifying the underlying architectural and conceptual differences. so that they can compare and relate to the underlying concepts. -Describe the key concepts and requirements for a memory management system, including virtual memory, partitioning, paging, protection and performance. -Analyse problems that can be solved with understanding of API/libraries in an operating system context. Given a specific programming problem show, without reference to a resource, how operating system API脝s and libraries can be used to reduce the amount of code that has to be written to solve the problem. - Develop a simple I/O device driver, know the individual steps necessary to copy the contents of a memory buffer to a physical block on a hard disk, as a formal driver. Affective (Attitudes and Values) Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Formal lectures, laboratory based assignments and projects, laboratory based tutorials. Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: A. Silberschatz (2005) Operating System Concepts, Wiley Other Texts: W. Stallings (2008) Operating Systems: Internals and Design Principles, Prentice Hall A. Tanenbaum (2007) Modern Operating Systems, Prentice Hall Programmes Semester(s) Module is Offered: Module Leader: [email protected]",
"question": "What will I learn in the operating systems 2 module?",
"answers": [
{
"text": "Internals and Design Principles",
"answer_start": 1597
}
]
},
{
"id": "68",
"title": "OPERATING_SYSTEMS_2",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) - Be able to define the underlying concepts for computer operating system design. - Be able to identify concurrency problems in software examples and describe how they can be fixed using appropriate synchronisation mechanisms. -Compare the features of two separate operating systems (Unix and WIN-32) by identifying the underlying architectural and conceptual differences. so that they can compare and relate to the underlying concepts. -Describe the key concepts and requirements for a memory management system, including virtual memory, partitioning, paging, protection and performance. -Analyse problems that can be solved with understanding of API/libraries in an operating system context. Given a specific programming problem show, without reference to a resource, how operating system API脝s and libraries can be used to reduce the amount of code that has to be written to solve the problem. - Develop a simple I/O device driver, know the individual steps necessary to copy the contents of a memory buffer to a physical block on a hard disk, as a formal driver. Affective (Attitudes and Values) Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Formal lectures, laboratory based assignments and projects, laboratory based tutorials. Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: A. Silberschatz (2005) Operating System Concepts, Wiley Other Texts: W. Stallings (2008) Operating Systems: Internals and Design Principles, Prentice Hall A. Tanenbaum (2007) Modern Operating Systems, Prentice Hall Programmes Semester(s) Module is Offered: Module Leader: [email protected]",
"question": "How the operating systems 2 module will be taught?",
"answers": [
{
"text": "Formal lectures, laboratory based assignments and projects, laboratory based tutorials",
"answer_start": 1323
}
]
},
{
"id": "69",
"title": "OPERATING_SYSTEMS_2",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) - Be able to define the underlying concepts for computer operating system design. - Be able to identify concurrency problems in software examples and describe how they can be fixed using appropriate synchronisation mechanisms. -Compare the features of two separate operating systems (Unix and WIN-32) by identifying the underlying architectural and conceptual differences. so that they can compare and relate to the underlying concepts. -Describe the key concepts and requirements for a memory management system, including virtual memory, partitioning, paging, protection and performance. -Analyse problems that can be solved with understanding of API/libraries in an operating system context. Given a specific programming problem show, without reference to a resource, how operating system API脝s and libraries can be used to reduce the amount of code that has to be written to solve the problem. - Develop a simple I/O device driver, know the individual steps necessary to copy the contents of a memory buffer to a physical block on a hard disk, as a formal driver. Affective (Attitudes and Values) Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Formal lectures, laboratory based assignments and projects, laboratory based tutorials. Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: A. Silberschatz (2005) Operating System Concepts, Wiley Other Texts: W. Stallings (2008) Operating Systems: Internals and Design Principles, Prentice Hall A. Tanenbaum (2007) Modern Operating Systems, Prentice Hall Programmes Semester(s) Module is Offered: Module Leader: [email protected]",
"question": "How can I contact the lecturer for the operating systems 2 module?",
"answers": [
{
"text": "[email protected]",
"answer_start": 1761
}
]
},
{
"id": "70",
"title": "DISTRIBUTED_SYSTEMS",
"context": "Module Code - Title: CE4208 - DISTRIBUTED SYSTEMS Prerequisite Modules: CE4607 CE4206 Rationale and Purpose of the Module: This module is designed to provide students with a framework for comparing emerging distributed systems, as well as an understanding of the algorithms necessary to support a distributed system. Computing models and data communications will be studied, as well as software development issues relating to the development of distributed applications. Syllabus: To introduces application design principles and techniques using available web-based technologies. (e.g SOAP, Microsoft.NET, Java Services). Reliability and security issues of distributed applications are addressed. Use of cookies and the covert use of applications to provide a community-wide service. Characterization of Distributed Systems. Tools and technologies used to develop distributed applications. Mechanisms to secure applications from malicious attacks and errant processes. Component based software development (e.g. CORBA, JavaBeans). Service portability via virtual servers. Replication and Fault Tolerance. Study of evolving Web services. The role of the hidden internet for intelligence gathering. Remotely hosted application environments.",
"question": "What is the code for the distributed systems module?",
"answers": [
{
"text": "CE4208",
"answer_start": 21
}
]
},
{
"id": "71",
"title": "DISTRIBUTED_SYSTEMS",
"context": "Module Code - Title: CE4208 - DISTRIBUTED SYSTEMS Prerequisite Modules: CE4607 CE4206 Rationale and Purpose of the Module: This module is designed to provide students with a framework for comparing emerging distributed systems, as well as an understanding of the algorithms necessary to support a distributed system. Computing models and data communications will be studied, as well as software development issues relating to the development of distributed applications. Syllabus: To introduces application design principles and techniques using available web-based technologies. (e.g SOAP, Microsoft.NET, Java Services). Reliability and security issues of distributed applications are addressed. Use of cookies and the covert use of applications to provide a community-wide service. Characterization of Distributed Systems. Tools and technologies used to develop distributed applications. Mechanisms to secure applications from malicious attacks and errant processes. Component based software development (e.g. CORBA, JavaBeans). Service portability via virtual servers. Replication and Fault Tolerance. Study of evolving Web services. The role of the hidden internet for intelligence gathering. Remotely hosted application environments.",
"question": "Are there any prerequisite modules for the distributed systems module?",
"answers": [
{
"text": "CE4607 CE4206",
"answer_start": 72
}
]
},
{
"id": "72",
"title": "DISTRIBUTED_SYSTEMS",
"context": "Module Code - Title: CE4208 - DISTRIBUTED SYSTEMS Prerequisite Modules: CE4607 CE4206 Rationale and Purpose of the Module: This module is designed to provide students with a framework for comparing emerging distributed systems, as well as an understanding of the algorithms necessary to support a distributed system. Computing models and data communications will be studied, as well as software development issues relating to the development of distributed applications. Syllabus: To introduces application design principles and techniques using available web-based technologies. (e.g SOAP, Microsoft.NET, Java Services). Reliability and security issues of distributed applications are addressed. Use of cookies and the covert use of applications to provide a community-wide service. Characterization of Distributed Systems. Tools and technologies used to develop distributed applications. Mechanisms to secure applications from malicious attacks and errant processes. Component based software development (e.g. CORBA, JavaBeans). Service portability via virtual servers. Replication and Fault Tolerance. Study of evolving Web services. The role of the hidden internet for intelligence gathering. Remotely hosted application environments.",
"question": "What technologies will I learn about in the distributed systems module?",
"answers": [
{
"text": "Computing models and data communications",
"answer_start": 317
}
]
},
{
"id": "73",
"title": "DISTRIBUTED_SYSTEMS",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) Design at a high level a distributed application that meets given performance, security and reliability criteria Critically review existing web service frameworks (e.g SOAP, Microsift.Net) Identify potential threats to a company implementing a distributed application- based on web services Develop a list of design requirements for a distributed application to ensure that a companys assets are protected. Show an understanding of the capabilities of the various web service technologies that are available commercially or provided by the research community. Affective (Attitudes and Values) Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Lectures, Labs and Tutorials Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Tanenbaum A., & van Steen M (2007) Distributed Systems 驴 Principles and Design 2e, Prentice Hall Other Texts: Programmes Semester(s) Module is Offered: Module Leader: [email protected]",
"question": "How will the distributed systems module be taught and delivered?",
"answers": [
{
"text": "Lectures, Labs and Tutorials",
"answer_start": 814
}
]
},
{
"id": "74",
"title": "DISTRIBUTED_SYSTEMS",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) Design at a high level a distributed application that meets given performance, security and reliability criteria Critically review existing web service frameworks (e.g SOAP, Microsift.Net) Identify potential threats to a company implementing a distributed application- based on web services Develop a list of design requirements for a distributed application to ensure that a companys assets are protected. Show an understanding of the capabilities of the various web service technologies that are available commercially or provided by the research community. Affective (Attitudes and Values) Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Lectures, Labs and Tutorials Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Tanenbaum A., & van Steen M (2007) Distributed Systems 驴 Principles and Design 2e, Prentice Hall Other Texts: Programmes Semester(s) Module is Offered: Module Leader: [email protected]",
"question": "What book I need for the distributed systems module?",
"answers": [
{
"text": "Principles and Design 2e",
"answer_start": 978
}
]
},
{
"id": "75",
"title": "DISTRIBUTED_SYSTEMS",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) Design at a high level a distributed application that meets given performance, security and reliability criteria Critically review existing web service frameworks (e.g SOAP, Microsift.Net) Identify potential threats to a company implementing a distributed application- based on web services Develop a list of design requirements for a distributed application to ensure that a companys assets are protected. Show an understanding of the capabilities of the various web service technologies that are available commercially or provided by the research community. Affective (Attitudes and Values) Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Lectures, Labs and Tutorials Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Tanenbaum A., & van Steen M (2007) Distributed Systems 驴 Principles and Design 2e, Prentice Hall Other Texts: Programmes Semester(s) Module is Offered: Module Leader: [email protected]",
"question": "How can I contact the lecturer for the distributed systems module?",
"answers": [
{
"text": "[email protected]",
"answer_start": 1088
}
]
},
{
"id": "76",
"title": "COMPUTER_ARCHITECTURE",
"context": "Module Code - Title: CE4518 - COMPUTER ARCHITECTURE Prerequisite Modules: CE4517 Rationale and Purpose of the Module: To provide a grounding in the analytic study of computer architecture and an introduction to various architectural styles, e.g., CISC, RISC, and variousnon-von Neumann architectures. Syllabus: Review of Von-Neumann architecture: Brief discussion of evolution in processor design from 1940's to today. Computer classifications. Flynn's taxonomy: SISD, SIMD, MIMD. Computer performance measurement: Execution time and clock cycles per instruction (CPI). MIPs, MFLOPs. Benchmarks: Dhrystone, Whetstone. Kernels: Livermore loops, Linpack, SPECmarks. Floating point arithmetic: IEEE 754. Addition. Rounding. Denormalised numbers. Multiplication. Iterative division. Precision. Instruction set design and architecture: Classification. Register machines. Addressing modes. The role of high-level languages and compilers in determining instruction set architecture, semantic gap, high-level language architecture, CISC and RISC architectures. Processor implementation techniques: Datapath. Execution steps. Control: hardwired, microcoded. Handling exceptions. Pipelining: Hazards in pipelines. CISC and RISC pipelines. Multicycle pipelines (superpipelining). Dynamic scheduling. Scoreboarding. Tomasulo's algorithm. Instruction level parallelism. Superscalar architecture. VLIW. Software pipelining and trace scheduling. Memory hierarchy design: Register windows. Caches: strategies, replacement policies, block size. Main memory: width, interleaving. Virtual memory: page tables, translation lookaside buffers.",
"question": "What is the code for the computer architecture module?",
"answers": [
{
"text": "CE4518",
"answer_start": 21
}
]
},
{
"id": "77",
"title": "COMPUTER_ARCHITECTURE",
"context": "Module Code - Title: CE4518 - COMPUTER ARCHITECTURE Prerequisite Modules: CE4517 Rationale and Purpose of the Module: To provide a grounding in the analytic study of computer architecture and an introduction to various architectural styles, e.g., CISC, RISC, and variousnon-von Neumann architectures. Syllabus: Review of Von-Neumann architecture: Brief discussion of evolution in processor design from 1940's to today. Computer classifications. Flynn's taxonomy: SISD, SIMD, MIMD. Computer performance measurement: Execution time and clock cycles per instruction (CPI). MIPs, MFLOPs. Benchmarks: Dhrystone, Whetstone. Kernels: Livermore loops, Linpack, SPECmarks. Floating point arithmetic: IEEE 754. Addition. Rounding. Denormalised numbers. Multiplication. Iterative division. Precision. Instruction set design and architecture: Classification. Register machines. Addressing modes. The role of high-level languages and compilers in determining instruction set architecture, semantic gap, high-level language architecture, CISC and RISC architectures. Processor implementation techniques: Datapath. Execution steps. Control: hardwired, microcoded. Handling exceptions. Pipelining: Hazards in pipelines. CISC and RISC pipelines. Multicycle pipelines (superpipelining). Dynamic scheduling. Scoreboarding. Tomasulo's algorithm. Instruction level parallelism. Superscalar architecture. VLIW. Software pipelining and trace scheduling. Memory hierarchy design: Register windows. Caches: strategies, replacement policies, block size. Main memory: width, interleaving. Virtual memory: page tables, translation lookaside buffers.",
"question": "Are there any prerequisite modules for the computer architecture module?",
"answers": [
{
"text": "CE4517",
"answer_start": 74
}
]
},
{
"id": "78",
"title": "COMPUTER_ARCHITECTURE",
"context": "Module Code - Title: CE4518 - COMPUTER ARCHITECTURE Prerequisite Modules: CE4517 Rationale and Purpose of the Module: To provide a grounding in the analytic study of computer architecture and an introduction to various architectural styles, e.g., CISC, RISC, and variousnon-von Neumann architectures. Syllabus: Review of Von-Neumann architecture: Brief discussion of evolution in processor design from 1940's to today. Computer classifications. Flynn's taxonomy: SISD, SIMD, MIMD. Computer performance measurement: Execution time and clock cycles per instruction (CPI). MIPs, MFLOPs. Benchmarks: Dhrystone, Whetstone. Kernels: Livermore loops, Linpack, SPECmarks. Floating point arithmetic: IEEE 754. Addition. Rounding. Denormalised numbers. Multiplication. Iterative division. Precision. Instruction set design and architecture: Classification. Register machines. Addressing modes. The role of high-level languages and compilers in determining instruction set architecture, semantic gap, high-level language architecture, CISC and RISC architectures. Processor implementation techniques: Datapath. Execution steps. Control: hardwired, microcoded. Handling exceptions. Pipelining: Hazards in pipelines. CISC and RISC pipelines. Multicycle pipelines (superpipelining). Dynamic scheduling. Scoreboarding. Tomasulo's algorithm. Instruction level parallelism. Superscalar architecture. VLIW. Software pipelining and trace scheduling. Memory hierarchy design: Register windows. Caches: strategies, replacement policies, block size. Main memory: width, interleaving. Virtual memory: page tables, translation lookaside buffers.",
"question": "What is the purpose of the computer architecture module?",
"answers": [
{
"text": "To provide a grounding in the analytic study of computer architecture",
"answer_start": 118
}
]
},
{
"id": "79",
"title": "COMPUTER_ARCHITECTURE",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) 1. Evaluate the impact on CPU performance of instruction set design 2. Evaluate the merits and demerits of various computer performance benchmarks 3. Evaluate the performance characteristics of computer arithmetic algorithms 4. Analyse and compare the performance of various caching algorithms 5. Describe the structure of pipelined and superscalar CPU microarchitectures Affective (Attitudes and Values) Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Lectures/Tutorials/Labs Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Hennessy, J.L. & Patterson, D.A. (2007) Computer Architecture: A Quantitative Approach, 4th ed., Elsevier Patterson, D.A. & Hennessy, J.L. (2005) Computer Organization & Design, 3rd ed., Elsevier Other Texts: Fisher, J.A. Faraboschi, P. & Young C. (2005) Embedded Computing: A VLIW Approach to ARchitecture, Compilers & Tools, Elsevier Shen, J.P. & Lipasti, M.H. (2005) Modern Processor Design: Fundamentals of Superscalar Processors, McGraw-Hill Ercegovac, M.D. & Lang, T. (2004) Digital Arithmetic, Elsevier Stines, J.E. (2004) Digital Computer Arithmetic Datapath Design Using Verilog HDL, Kluwer Lee, S. & Sjoholm, S. (2003) Design of Computers and Other Complex Digital Devices with VHDL for Designers, Prentice Hall Koren, I. (2002) Computer Arithmetic Algorithms, 2nd ed., A K Peters Ltd Shriver, B. & Smith, B. (1998) The Anatomy of a High-Performance Microprocessor, IEEE Computer Society Press Programmes Semester(s) Module is Offered: Module Leader: [email protected]",
"question": "What will I learn in the computer architecture module?",
"answers": [
{
"text": "The Anatomy of a High-Performance Microprocessor",
"answer_start": 1555
}
]
},
{
"id": "80",
"title": "COMPUTER_ARCHITECTURE",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) 1. Evaluate the impact on CPU performance of instruction set design 2. Evaluate the merits and demerits of various computer performance benchmarks 3. Evaluate the performance characteristics of computer arithmetic algorithms 4. Analyse and compare the performance of various caching algorithms 5. Describe the structure of pipelined and superscalar CPU microarchitectures Affective (Attitudes and Values) Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Lectures/Tutorials/Labs Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Hennessy, J.L. & Patterson, D.A. (2007) Computer Architecture: A Quantitative Approach, 4th ed., Elsevier Patterson, D.A. & Hennessy, J.L. (2005) Computer Organization & Design, 3rd ed., Elsevier Other Texts: Fisher, J.A. Faraboschi, P. & Young C. (2005) Embedded Computing: A VLIW Approach to ARchitecture, Compilers & Tools, Elsevier Shen, J.P. & Lipasti, M.H. (2005) Modern Processor Design: Fundamentals of Superscalar Processors, McGraw-Hill Ercegovac, M.D. & Lang, T. (2004) Digital Arithmetic, Elsevier Stines, J.E. (2004) Digital Computer Arithmetic Datapath Design Using Verilog HDL, Kluwer Lee, S. & Sjoholm, S. (2003) Design of Computers and Other Complex Digital Devices with VHDL for Designers, Prentice Hall Koren, I. (2002) Computer Arithmetic Algorithms, 2nd ed., A K Peters Ltd Shriver, B. & Smith, B. (1998) The Anatomy of a High-Performance Microprocessor, IEEE Computer Society Press Programmes Semester(s) Module is Offered: Module Leader: [email protected]",
"question": "What textbook I need to read for the computer architecture module?",
"answers": [
{
"text": "A Quantitative Approach",
"answer_start": 792
}
]
},
{
"id": "81",
"title": "COMPUTER_ARCHITECTURE",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) 1. Evaluate the impact on CPU performance of instruction set design 2. Evaluate the merits and demerits of various computer performance benchmarks 3. Evaluate the performance characteristics of computer arithmetic algorithms 4. Analyse and compare the performance of various caching algorithms 5. Describe the structure of pipelined and superscalar CPU microarchitectures Affective (Attitudes and Values) Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Lectures/Tutorials/Labs Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Hennessy, J.L. & Patterson, D.A. (2007) Computer Architecture: A Quantitative Approach, 4th ed., Elsevier Patterson, D.A. & Hennessy, J.L. (2005) Computer Organization & Design, 3rd ed., Elsevier Other Texts: Fisher, J.A. Faraboschi, P. & Young C. (2005) Embedded Computing: A VLIW Approach to ARchitecture, Compilers & Tools, Elsevier Shen, J.P. & Lipasti, M.H. (2005) Modern Processor Design: Fundamentals of Superscalar Processors, McGraw-Hill Ercegovac, M.D. & Lang, T. (2004) Digital Arithmetic, Elsevier Stines, J.E. (2004) Digital Computer Arithmetic Datapath Design Using Verilog HDL, Kluwer Lee, S. & Sjoholm, S. (2003) Design of Computers and Other Complex Digital Devices with VHDL for Designers, Prentice Hall Koren, I. (2002) Computer Arithmetic Algorithms, 2nd ed., A K Peters Ltd Shriver, B. & Smith, B. (1998) The Anatomy of a High-Performance Microprocessor, IEEE Computer Society Press Programmes Semester(s) Module is Offered: Module Leader: [email protected]",
"question": "How can I contact the lecturer for the computer architecture module?",
"answers": [
{
"text": "[email protected]",
"answer_start": 1690
}
]
},
{
"id": "82",
"title": "COMPUTER_NETWORKS_1",
"context": "Module Code - Title: CE4607 - COMPUTER NETWORKS 1 Prerequisite Modules: EE4616 Rationale and Purpose of the Module: This module provides a unified view of the field of computer communications and networks. The module seeks to integrate a number of topics introduced in earlier parts of the course and addresses the analysis, design and performance evaluation of data communication systems. The module covers communications within and between computer systems, and communications protocols and standards. Syllabus: * [Introduction to Data and Computer Communications] Communications tasks; Protocol elements, characteristics, and functions; Protocol architectures; Reference communications models overview: OSI vs. TCP/IP (layers脝 description and functions, PDU encapsulation). * [Physical Transmission] Transmission modes (simplex, half duplex, full duplex) and transmission types (baseband, broadband); Analogue and digital signals; Transmission impairments (attenuation, delay distortion, noise); Channel capacity; Data encoding and modulation; Physical interfacing; Asynchronous & synchronous transmission; Transmission media; Multiplexing techniques (FDM, TDM, WDM). * [Link-by-Link Communication] Line disciplines (ENQ/ACK, poll/select); Framing; Frame synchronization & data transparency, Flow control; Error control; Addressing; Link management; Protocol examples (character-oriented, byte-count, bit-oriented). * [Network Services] Switching (circuit-, message-, packet switching); Addressing (classful vs. classless IP addressing); NAT operation (static and dynamic); IP subnetting and supernetting; Routing (concepts and principles; routing algorithms 没 flooding, static, dynamic; central and distributed control; distance vector vs. link state routing; hierarchical routing; routing protocols examples: interior vs. exterior); Congestion control; QoS provision; IP protocol: main functions and operation (IPv4 vs. IPv6); Mobile IP; Address resolution with ARP and RARP; Internet multicasting (MBone operation) and group management (IGMP protocol); Control and assistance mechanisms (ICMP protocol: v4 vs. v6). Modular design of protocols. * [Transport Services] Overview (connection-oriented vs. connectionless; segmentation and re-assembly; end-to-end delivery, flow control & buffering; crash recovery); Unreliable datagram transport with UDP; Real-time transport with RTP and RTCP; Reliable connection-oriented transport with TCP and SCTP; Wireless TCP; Modular design of protocols. * [End-to-End Communication] Session management (SIP and SDP protocols); Data presentation (ASN.1 and NVT); Client-server communication model; Domain Name System (DNS); TCP/IP configuration: static (BOOTP protocol) vs. dynamic (DHCP protocol); Terminal networking with Telnet; File transfer with FTP and TFTP; E-mail service (SMTP, POP, IMAP protocols); Browsing with HTTP; Network management with SNMP. * [Practical Implementation] Building and testing different types of patch cables; Serial interface configuration; Device configuration: IOS software, managing configuration files, updating software; Router configuration: initialisation, commands and modes of operation; Routing protocols脝 configuration, operation and evaluation: RIP, IGRP etc.; Network configuration: testing established connectivity and routes. Analysing and interpreting IP addresses and subnets; Scaling the IP address space: CIDR, private addressing, secondary IP addressing, MTU and fragmentation; NAT configuration; TCP/IP protocols configuration and operation.",
"question": "What is the code for the computer networks 1 module?",
"answers": [
{
"text": "CE4607",
"answer_start": 21
}
]
},
{
"id": "83",
"title": "COMPUTER_NETWORKS_1",
"context": "Module Code - Title: CE4607 - COMPUTER NETWORKS 1 Prerequisite Modules: EE4616 Rationale and Purpose of the Module: This module provides a unified view of the field of computer communications and networks. The module seeks to integrate a number of topics introduced in earlier parts of the course and addresses the analysis, design and performance evaluation of data communication systems. The module covers communications within and between computer systems, and communications protocols and standards. Syllabus: * [Introduction to Data and Computer Communications] Communications tasks; Protocol elements, characteristics, and functions; Protocol architectures; Reference communications models overview: OSI vs. TCP/IP (layers脝 description and functions, PDU encapsulation). * [Physical Transmission] Transmission modes (simplex, half duplex, full duplex) and transmission types (baseband, broadband); Analogue and digital signals; Transmission impairments (attenuation, delay distortion, noise); Channel capacity; Data encoding and modulation; Physical interfacing; Asynchronous & synchronous transmission; Transmission media; Multiplexing techniques (FDM, TDM, WDM). * [Link-by-Link Communication] Line disciplines (ENQ/ACK, poll/select); Framing; Frame synchronization & data transparency, Flow control; Error control; Addressing; Link management; Protocol examples (character-oriented, byte-count, bit-oriented). * [Network Services] Switching (circuit-, message-, packet switching); Addressing (classful vs. classless IP addressing); NAT operation (static and dynamic); IP subnetting and supernetting; Routing (concepts and principles; routing algorithms 没 flooding, static, dynamic; central and distributed control; distance vector vs. link state routing; hierarchical routing; routing protocols examples: interior vs. exterior); Congestion control; QoS provision; IP protocol: main functions and operation (IPv4 vs. IPv6); Mobile IP; Address resolution with ARP and RARP; Internet multicasting (MBone operation) and group management (IGMP protocol); Control and assistance mechanisms (ICMP protocol: v4 vs. v6). Modular design of protocols. * [Transport Services] Overview (connection-oriented vs. connectionless; segmentation and re-assembly; end-to-end delivery, flow control & buffering; crash recovery); Unreliable datagram transport with UDP; Real-time transport with RTP and RTCP; Reliable connection-oriented transport with TCP and SCTP; Wireless TCP; Modular design of protocols. * [End-to-End Communication] Session management (SIP and SDP protocols); Data presentation (ASN.1 and NVT); Client-server communication model; Domain Name System (DNS); TCP/IP configuration: static (BOOTP protocol) vs. dynamic (DHCP protocol); Terminal networking with Telnet; File transfer with FTP and TFTP; E-mail service (SMTP, POP, IMAP protocols); Browsing with HTTP; Network management with SNMP. * [Practical Implementation] Building and testing different types of patch cables; Serial interface configuration; Device configuration: IOS software, managing configuration files, updating software; Router configuration: initialisation, commands and modes of operation; Routing protocols脝 configuration, operation and evaluation: RIP, IGRP etc.; Network configuration: testing established connectivity and routes. Analysing and interpreting IP addresses and subnets; Scaling the IP address space: CIDR, private addressing, secondary IP addressing, MTU and fragmentation; NAT configuration; TCP/IP protocols configuration and operation.",
"question": "What are the prerequisite modules for the computer networks 1 module?",
"answers": [
{
"text": "EE4616 Rationale and Purpose of the Module",
"answer_start": 72
}
]
},
{
"id": "84",
"title": "COMPUTER_NETWORKS_1",
"context": "Module Code - Title: CE4607 - COMPUTER NETWORKS 1 Prerequisite Modules: EE4616 Rationale and Purpose of the Module: This module provides a unified view of the field of computer communications and networks. The module seeks to integrate a number of topics introduced in earlier parts of the course and addresses the analysis, design and performance evaluation of data communication systems. The module covers communications within and between computer systems, and communications protocols and standards. Syllabus: * [Introduction to Data and Computer Communications] Communications tasks; Protocol elements, characteristics, and functions; Protocol architectures; Reference communications models overview: OSI vs. TCP/IP (layers脝 description and functions, PDU encapsulation). * [Physical Transmission] Transmission modes (simplex, half duplex, full duplex) and transmission types (baseband, broadband); Analogue and digital signals; Transmission impairments (attenuation, delay distortion, noise); Channel capacity; Data encoding and modulation; Physical interfacing; Asynchronous & synchronous transmission; Transmission media; Multiplexing techniques (FDM, TDM, WDM). * [Link-by-Link Communication] Line disciplines (ENQ/ACK, poll/select); Framing; Frame synchronization & data transparency, Flow control; Error control; Addressing; Link management; Protocol examples (character-oriented, byte-count, bit-oriented). * [Network Services] Switching (circuit-, message-, packet switching); Addressing (classful vs. classless IP addressing); NAT operation (static and dynamic); IP subnetting and supernetting; Routing (concepts and principles; routing algorithms 没 flooding, static, dynamic; central and distributed control; distance vector vs. link state routing; hierarchical routing; routing protocols examples: interior vs. exterior); Congestion control; QoS provision; IP protocol: main functions and operation (IPv4 vs. IPv6); Mobile IP; Address resolution with ARP and RARP; Internet multicasting (MBone operation) and group management (IGMP protocol); Control and assistance mechanisms (ICMP protocol: v4 vs. v6). Modular design of protocols. * [Transport Services] Overview (connection-oriented vs. connectionless; segmentation and re-assembly; end-to-end delivery, flow control & buffering; crash recovery); Unreliable datagram transport with UDP; Real-time transport with RTP and RTCP; Reliable connection-oriented transport with TCP and SCTP; Wireless TCP; Modular design of protocols. * [End-to-End Communication] Session management (SIP and SDP protocols); Data presentation (ASN.1 and NVT); Client-server communication model; Domain Name System (DNS); TCP/IP configuration: static (BOOTP protocol) vs. dynamic (DHCP protocol); Terminal networking with Telnet; File transfer with FTP and TFTP; E-mail service (SMTP, POP, IMAP protocols); Browsing with HTTP; Network management with SNMP. * [Practical Implementation] Building and testing different types of patch cables; Serial interface configuration; Device configuration: IOS software, managing configuration files, updating software; Router configuration: initialisation, commands and modes of operation; Routing protocols脝 configuration, operation and evaluation: RIP, IGRP etc.; Network configuration: testing established connectivity and routes. Analysing and interpreting IP addresses and subnets; Scaling the IP address space: CIDR, private addressing, secondary IP addressing, MTU and fragmentation; NAT configuration; TCP/IP protocols configuration and operation.",
"question": "What does the computer networks 1 module cover?",
"answers": [
{
"text": "communications within and between computer systems, and communications protocols and standards",
"answer_start": 410
}
]
},
{
"id": "85",
"title": "COMPUTER_NETWORKS_1",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) 1. Locate, analyse and assess different elements of communication protocols used in computer networks. 2. Differentiate between different communication services and identify suitable ones for use in computer networks. 3. Given requirements for computer network, find correct solutions for internetworking / interoperability, including subnetting and supernetting, verification of addresses, and traffic filtering. 4. Given a computer (inter)network topology, identify problems that a routing algorithm may encounter, describe techniques to reduce these problems, construct correct routing tables (find optimal path between any two end points) without reference to a source. 5. Given requirements for performance and reliability of computer network, define, categorise, discuss and employ different techniques for error control, flow control, QoS control, and congestion control. Affective (Attitudes and Values) Psychomotor (Physical Skills) 1. Load with software and configure layer 2 & 3 networking devices, i.e. switches and routers. 2. Understand how to configure, connect, and troubleshoot IP networks. How the Module will be Taught and what will be the Learning Experiences of the Students: Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts:Tanenbaum A.S. (2003) Computer Networks, 4th ed., Prentice Hall Stallings, W. (2007) Data and Computer Communications, 8th ed., Prentice Hall Other Texts: Forouzan B.A. (2005) TCP/IP Protocol Suit, 2nd updated ed., McGraw-Hill Forouzan B.A. (2007) Data Communications and Networking, 4th ed., McGraw-Hill Programmes Semester(s) Module is Offered: Module Leader: [email protected]",
"question": "What will I be able to do in the computer networks 1 module?",
"answers": [
{
"text": "Understand how to configure, connect, and troubleshoot IP networks",
"answer_start": 1142
}
]
},
{
"id": "86",
"title": "COMPUTER_NETWORKS_1",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) 1. Locate, analyse and assess different elements of communication protocols used in computer networks. 2. Differentiate between different communication services and identify suitable ones for use in computer networks. 3. Given requirements for computer network, find correct solutions for internetworking / interoperability, including subnetting and supernetting, verification of addresses, and traffic filtering. 4. Given a computer (inter)network topology, identify problems that a routing algorithm may encounter, describe techniques to reduce these problems, construct correct routing tables (find optimal path between any two end points) without reference to a source. 5. Given requirements for performance and reliability of computer network, define, categorise, discuss and employ different techniques for error control, flow control, QoS control, and congestion control. Affective (Attitudes and Values) Psychomotor (Physical Skills) 1. Load with software and configure layer 2 & 3 networking devices, i.e. switches and routers. 2. Understand how to configure, connect, and troubleshoot IP networks. How the Module will be Taught and what will be the Learning Experiences of the Students: Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts:Tanenbaum A.S. (2003) Computer Networks, 4th ed., Prentice Hall Stallings, W. (2007) Data and Computer Communications, 8th ed., Prentice Hall Other Texts: Forouzan B.A. (2005) TCP/IP Protocol Suit, 2nd updated ed., McGraw-Hill Forouzan B.A. (2007) Data Communications and Networking, 4th ed., McGraw-Hill Programmes Semester(s) Module is Offered: Module Leader: [email protected]",
"question": "What are the prime texts that I need to read for the computer networks 1 module?",
"answers": [
{
"text": "Data and Computer Communications",
"answer_start": 1462
}
]
},
{
"id": "87",
"title": "COMPUTER_NETWORKS_1",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) 1. Locate, analyse and assess different elements of communication protocols used in computer networks. 2. Differentiate between different communication services and identify suitable ones for use in computer networks. 3. Given requirements for computer network, find correct solutions for internetworking / interoperability, including subnetting and supernetting, verification of addresses, and traffic filtering. 4. Given a computer (inter)network topology, identify problems that a routing algorithm may encounter, describe techniques to reduce these problems, construct correct routing tables (find optimal path between any two end points) without reference to a source. 5. Given requirements for performance and reliability of computer network, define, categorise, discuss and employ different techniques for error control, flow control, QoS control, and congestion control. Affective (Attitudes and Values) Psychomotor (Physical Skills) 1. Load with software and configure layer 2 & 3 networking devices, i.e. switches and routers. 2. Understand how to configure, connect, and troubleshoot IP networks. How the Module will be Taught and what will be the Learning Experiences of the Students: Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts:Tanenbaum A.S. (2003) Computer Networks, 4th ed., Prentice Hall Stallings, W. (2007) Data and Computer Communications, 8th ed., Prentice Hall Other Texts: Forouzan B.A. (2005) TCP/IP Protocol Suit, 2nd updated ed., McGraw-Hill Forouzan B.A. (2007) Data Communications and Networking, 4th ed., McGraw-Hill Programmes Semester(s) Module is Offered: Module Leader: [email protected]",
"question": "How can I contact the lecturer for the computer networks 1 module?",
"answers": [
{
"text": "[email protected]",
"answer_start": 1739
}
]
},
{
"id": "88",
"title": "COMPUTER_SOFTWARE_1",
"context": "Module Code - Title: CE4701 - COMPUTER SOFTWARE 1 Prerequisite Modules: Rationale and Purpose of the Module: Introduce students to a high level object-oriented programming language and its software development environment Syllabus: The focus of this module is to introduce a modern high level object-oriented programming language to enable the student to develop the programming skills necessary to write simple but useful applications. The following topics will be covered: Introduction to software development. Short comparative study of different programming languages. Simple program design techniques e.g. flowcharts, Basic data types, control statements, methods, scope. Relationship between the program, the run time environment and the operating system. Introduction to programming language documentation. Introduction to Class Libraries. Interactive Development Environments. Introduction and demonstration of a low level graphics toolkit. Basic test practices and test case definition.",
"question": "What is the code for the computer software 1 module?",
"answers": [
{
"text": "CE4701",
"answer_start": 21
}
]
},
{
"id": "89",
"title": "COMPUTER_SOFTWARE_1",
"context": "Module Code - Title: CE4701 - COMPUTER SOFTWARE 1 Prerequisite Modules: Rationale and Purpose of the Module: Introduce students to a high level object-oriented programming language and its software development environment Syllabus: The focus of this module is to introduce a modern high level object-oriented programming language to enable the student to develop the programming skills necessary to write simple but useful applications. The following topics will be covered: Introduction to software development. Short comparative study of different programming languages. Simple program design techniques e.g. flowcharts, Basic data types, control statements, methods, scope. Relationship between the program, the run time environment and the operating system. Introduction to programming language documentation. Introduction to Class Libraries. Interactive Development Environments. Introduction and demonstration of a low level graphics toolkit. Basic test practices and test case definition.",
"question": "What is the purpose of the computer software 1 module?",
"answers": [
{
"text": "Introduce students to a high level object-oriented programming language",
"answer_start": 109
}
]
},
{
"id": "90",
"title": "COMPUTER_SOFTWARE_1",
"context": "Module Code - Title: CE4701 - COMPUTER SOFTWARE 1 Prerequisite Modules: Rationale and Purpose of the Module: Introduce students to a high level object-oriented programming language and its software development environment Syllabus: The focus of this module is to introduce a modern high level object-oriented programming language to enable the student to develop the programming skills necessary to write simple but useful applications. The following topics will be covered: Introduction to software development. Short comparative study of different programming languages. Simple program design techniques e.g. flowcharts, Basic data types, control statements, methods, scope. Relationship between the program, the run time environment and the operating system. Introduction to programming language documentation. Introduction to Class Libraries. Interactive Development Environments. Introduction and demonstration of a low level graphics toolkit. Basic test practices and test case definition.",
"question": "What program design techniques will I learn in the computer software 1 module?",
"answers": [
{
"text": "flowcharts, Basic data types, control statements, methods, scope",
"answer_start": 612
}
]
},
{
"id": "91",
"title": "COMPUTER_SOFTWARE_1",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) 1. Given a problem definition, formulate an algorithm to provide a solution. 2. Describe an algorithm using pseudocode. 3. Code a program solution using structured programming constructs. 4. Test and debug a program 5. Apply top-down design and modular design to a problem and employ this structure in a program. Affective (Attitudes and Values) Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Lectures, Labs and individual software assignments Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Other Texts: Deitel P. & Deitel H. (2017) Java-How to Program, Early Objects, 11e (8e+ will suffice), Pearson Liang, Y. D. (2020) Introduction to Java Programming, Pearson Savitch W. (2018) Java: An Introduction to Problem Solving and Programming, Pearson Malik D. S & Nair P. S. (2012) Java Programming: From Problem Analysis to Program Design, Thomson Programmes Semester(s) Module is Offered: Module Leader: [email protected]",
"question": "What will I be able to do in the computer software 1 module?",
"answers": [
{
"text": "Test and debug",
"answer_start": 293
}
]
},
{
"id": "92",
"title": "COMPUTER_SOFTWARE_1",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) 1. Given a problem definition, formulate an algorithm to provide a solution. 2. Describe an algorithm using pseudocode. 3. Code a program solution using structured programming constructs. 4. Test and debug a program 5. Apply top-down design and modular design to a problem and employ this structure in a program. Affective (Attitudes and Values) Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Lectures, Labs and individual software assignments Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Other Texts: Deitel P. & Deitel H. (2017) Java-How to Program, Early Objects, 11e (8e+ will suffice), Pearson Liang, Y. D. (2020) Introduction to Java Programming, Pearson Savitch W. (2018) Java: An Introduction to Problem Solving and Programming, Pearson Malik D. S & Nair P. S. (2012) Java Programming: From Problem Analysis to Program Design, Thomson Programmes Semester(s) Module is Offered: Module Leader: [email protected]",
"question": "How will the computer software 1 module be taught and delivered?",
"answers": [
{
"text": "Lectures, Labs",
"answer_start": 567
}
]
},
{
"id": "93",
"title": "COMPUTER_SOFTWARE_1",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) 1. Given a problem definition, formulate an algorithm to provide a solution. 2. Describe an algorithm using pseudocode. 3. Code a program solution using structured programming constructs. 4. Test and debug a program 5. Apply top-down design and modular design to a problem and employ this structure in a program. Affective (Attitudes and Values) Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Lectures, Labs and individual software assignments Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Other Texts: Deitel P. & Deitel H. (2017) Java-How to Program, Early Objects, 11e (8e+ will suffice), Pearson Liang, Y. D. (2020) Introduction to Java Programming, Pearson Savitch W. (2018) Java: An Introduction to Problem Solving and Programming, Pearson Malik D. S & Nair P. S. (2012) Java Programming: From Problem Analysis to Program Design, Thomson Programmes Semester(s) Module is Offered: Module Leader: [email protected]",
"question": "How do I contact the lecturer for the computer software 1 module?",
"answers": [
{
"text": "[email protected]",
"answer_start": 1108
}
]
},
{
"id": "94",
"title": "COMPUTER_SOFTWARE_2",
"context": "Module Code - Title: CE4702 - COMPUTER SOFTWARE 2 Prerequisite Modules: CE4701 Rationale and Purpose of the Module: Further the students knowledge of a modern object oriented programming language with particular emphasis on classes, objects and Graphical User Interfaces. Understand the concepts of inheritance and polymorphism. Develop the ability to produce moderately complex event driven programs with user interfaces developed using a graphical toolbox. Syllabus: The following topics will be covered: In depth study of the object oriented principles, abstraction, inheritance and polymorphism. Abstract data types including interfaces, abstract classes. Input and output including files and streams. Introduction to the use of regular expressions to manipulate text files Introduction to algorithms - efficiency, simple analysis and comparison Error handling techniques Binary trees Recursion Graphical user interfaces and development of event driven applications Unique global class naming and creation of class libraries Code documentation and code reviews Use case analysis",
"question": "What is the code for the computer software 2 module?",
"answers": [
{
"text": "CE4702",
"answer_start": 21
}
]
},
{
"id": "95",
"title": "COMPUTER_SOFTWARE_2",
"context": "Module Code - Title: CE4702 - COMPUTER SOFTWARE 2 Prerequisite Modules: CE4701 Rationale and Purpose of the Module: Further the students knowledge of a modern object oriented programming language with particular emphasis on classes, objects and Graphical User Interfaces. Understand the concepts of inheritance and polymorphism. Develop the ability to produce moderately complex event driven programs with user interfaces developed using a graphical toolbox. Syllabus: The following topics will be covered: In depth study of the object oriented principles, abstraction, inheritance and polymorphism. Abstract data types including interfaces, abstract classes. Input and output including files and streams. Introduction to the use of regular expressions to manipulate text files Introduction to algorithms - efficiency, simple analysis and comparison Error handling techniques Binary trees Recursion Graphical user interfaces and development of event driven applications Unique global class naming and creation of class libraries Code documentation and code reviews Use case analysis",
"question": "What are the prerequisite modules for the computer software 2 module?",
"answers": [
{
"text": "CE4701",
"answer_start": 72
}
]
},
{
"id": "96",
"title": "COMPUTER_SOFTWARE_2",
"context": "Module Code - Title: CE4702 - COMPUTER SOFTWARE 2 Prerequisite Modules: CE4701 Rationale and Purpose of the Module: Further the students knowledge of a modern object oriented programming language with particular emphasis on classes, objects and Graphical User Interfaces. Understand the concepts of inheritance and polymorphism. Develop the ability to produce moderately complex event driven programs with user interfaces developed using a graphical toolbox. Syllabus: The following topics will be covered: In depth study of the object oriented principles, abstraction, inheritance and polymorphism. Abstract data types including interfaces, abstract classes. Input and output including files and streams. Introduction to the use of regular expressions to manipulate text files Introduction to algorithms - efficiency, simple analysis and comparison Error handling techniques Binary trees Recursion Graphical user interfaces and development of event driven applications Unique global class naming and creation of class libraries Code documentation and code reviews Use case analysis",
"question": "What is the purpose of the computer software 2 module?",
"answers": [
{
"text": "Further the students knowledge of a modern object oriented programming language",
"answer_start": 116
}
]
},
{
"id": "97",
"title": "COMPUTER_SOFTWARE_2",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) 1. Decompose a problem into a set of classes, using the concepts of inheritance and polymorphism 2. Construct code, using existing class libraries, to implement specific programming problems 3. Demonstrate the use of regular expressions, error handling techniques and recursion. 4. Implement programs that manage dynamic data structures. 5. Implement applications with graphical user interfaces to accept dynamic data and modify the gui in response to an input 6. Demonstrate the use of software structuring techniques including use case analysis, code documentation and code reviews Affective (Attitudes and Values) Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Lectures, Labs and software development projects Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Other Texts: Dietel and Dietel (2020) Java - How to Program, Early Objects, 11e editions (8e+ suffices), Pearson Liang, Y. D. () Introduction to Java Programming, Pearson Savitch W. () Java: An Introduction to Problem Solving and Programming, Pearson Malik DS and Nair PS () Java Programming, From Problem Analysis to Program Design, Thomson Programmes Semester(s) Module is Offered: Module Leader: [email protected]",
"question": "What will I be able to do in the computer software 2 module?",
"answers": [
{
"text": "Construct code",
"answer_start": 202
}
]
},
{
"id": "98",
"title": "COMPUTER_SOFTWARE_2",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) 1. Decompose a problem into a set of classes, using the concepts of inheritance and polymorphism 2. Construct code, using existing class libraries, to implement specific programming problems 3. Demonstrate the use of regular expressions, error handling techniques and recursion. 4. Implement programs that manage dynamic data structures. 5. Implement applications with graphical user interfaces to accept dynamic data and modify the gui in response to an input 6. Demonstrate the use of software structuring techniques including use case analysis, code documentation and code reviews Affective (Attitudes and Values) Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Lectures, Labs and software development projects Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Other Texts: Dietel and Dietel (2020) Java - How to Program, Early Objects, 11e editions (8e+ suffices), Pearson Liang, Y. D. () Introduction to Java Programming, Pearson Savitch W. () Java: An Introduction to Problem Solving and Programming, Pearson Malik DS and Nair PS () Java Programming, From Problem Analysis to Program Design, Thomson Programmes Semester(s) Module is Offered: Module Leader: [email protected]",
"question": "What will I demonstrate in the computer software 2 module?",
"answers": [
{
"text": "use case analysis, code documentation and code reviews",
"answer_start": 633
}
]
},
{
"id": "99",
"title": "COMPUTER_SOFTWARE_2",
"context": "Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) 1. Decompose a problem into a set of classes, using the concepts of inheritance and polymorphism 2. Construct code, using existing class libraries, to implement specific programming problems 3. Demonstrate the use of regular expressions, error handling techniques and recursion. 4. Implement programs that manage dynamic data structures. 5. Implement applications with graphical user interfaces to accept dynamic data and modify the gui in response to an input 6. Demonstrate the use of software structuring techniques including use case analysis, code documentation and code reviews Affective (Attitudes and Values) Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Lectures, Labs and software development projects Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Other Texts: Dietel and Dietel (2020) Java - How to Program, Early Objects, 11e editions (8e+ suffices), Pearson Liang, Y. D. () Introduction to Java Programming, Pearson Savitch W. () Java: An Introduction to Problem Solving and Programming, Pearson Malik DS and Nair PS () Java Programming, From Problem Analysis to Program Design, Thomson Programmes Semester(s) Module is Offered: Module Leader: [email protected]",
"question": "How can I contact the lecturer for the computer software 2 module?",
"answers": [
{
"text": "[email protected]",
"answer_start": 1367
}
]
}
] |
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