Computer Science and Artificial Intelligence BSc (Hons) or MEng — 2027 entry

Module purpose:This module provides an introduction to the process of digital image formation in real and computer-generated imagery and builds on EEE1035 Programming in C. The module covers mathematical methods used to represent cameras, scene geometry and lighting in computer vision and computer graphics. The course introduces both the theoretical concepts and practical implementation of three-dimensional computer graphics used in visual effects, games and scientific visualisation. The practical implementation of computer graphics is introduced using the OpenGL libraries, which are widely used in industry. Some of the concepts developed in this module will be useful in other computer vision modules such as EEE3032: Computer Vision and Pattern Recognition. Expected prior learning: Learning equivalent to Year 1 and Year 2 Semester 1 of EE programmes.

This course introduces the core concepts of parallel computing by examining the algorithms and architectures that support it. Students will implement parallel solutions during our labs and evaluate their performance, gaining hands-on experience and insight into the challenges and potential tradeoffs involved. The course places particular emphasis on making algorithmic decisions that are informed by hardware characteristics, providing a strong foundation for designing high¿performance computing systems.

Computer networks are an essential part of almost all corporate computing facilities and even most domestic ones.  Interoperability is the key – all components must conform to the same hardware and packet specifications in order that they can be interconnected successfully.  This module introduces essential concepts about all the computer networking layering levels with some emphasis on the routing algorithms and implementation of network sensing. 

This module gives an introductory yet up-to-date description of the fundamental technologies of computational Intelligence, including evolutionary computation, neural computing and their applications. Main streams of evolutionary algorithms and meta-heuristics, including genetic algorithms, evolution strategies, genetic programming, particle swarm optimization will be taught. Basic neural network models and learning algorithms will be introduced. Interactions between evolution and learning, real-world applications to optimization and robotics, and recent advances will also be discussed. Good skill in Python programming, good knowledge in mathematics (calculus) are required.

Expected prior learning: Module EEE2041 – Computer Vision & Graphics, or equivalent learning about the geometric interpretation of Linear Algebra (e.g. homogeneous coordinates and matrices for point transformation e.g. rotation, translation, scaling). Module purpose: The module delivers a grounding in Computer Vision, suitable for students with a grounding in linear algebra similar to that provided by EEE2041 – Computer Vision & Graphics) and will help with modules such EEEM071 Advanced Topics in Computer Vision and Deep Learning. Content is presented as an application-focused tour of Computer Vision from the low-level (image processing), through to high level model fitting and object recognition.  

This module introduces general concepts of privacy enhancing technologies and aligns with key concepts recommended by the CyBoK. It will motivate the need for privacy in the modern world and touch on legal considerations, introduce concepts of transparency, control and confidentiality for privacy, and look at privacy preserving and democratic values. This module will also explore how these are realised in a range of applications.

Module purpose: Modern robotics brings together many aspects of engineering including electronics, hardware, software and AI. This leads to complex asynchronous systems that requires a systems engineering approach. The Robotics Operating System (ROS), is an extensive community built software suite that underpins most leading-edge robotics development. It provides extensive hardware interfacing and high-level functionality which allows complex systems engineering and control while abstracting away much of the complexity inherent to robotics systems design. This module will use ROS to provide a solid foundation in systems engineering based robotics.

This module will demonstrate fundamental concepts from the field of Natural Language Processing (NLP) and Computational Linguistics. It will also discuss some of the latest advances in NLP and Generative Artificial Intelligence with a focus on Language Models like BERT, T5, and GPT, and get student up to speed with current research. It will provide the necessary skills to enable students to build computational models for solving a range of problems, such as text classification, sequence classification, machine translation and building conversation agents. The students will learn how to build NLP pipelines for preparing training data and choosing appropriate algorithms and techniques to build such models. The module also focuses on aspects of ethical and trustworthy artificial intelligence with discussion on rigorous model evaluation and ethical considerations for computational modeling. Although traditional linguistic approaches will be mentioned, majority emphasis will be put on the state-of-the-art Deep Learning algorithms and Transfer Learning methods for building efficient and trustworthy NLP solutions. 

Expected prior learning: Module EEE2040 – Communications Networks or equivalent learning. Module purpose: The Internet is an important worldwide communications system; the module provides an in-depth treatment of current and evolving Internet protocols and standards, and the algorithms that underlie them. The module also permits further study on networking in modules such as EEEM018 Advanced Mobile Communication Systems, EEEM023 Network Service management and Control, EEEM032 Advanced Satellite Communication Techniques

Machine/Deep learning has emerged from computer science and artificial intelligence. It draws on methods from a variety of related subjects including statistics, applied mathematics and more specialized fields, such as pattern recognition and neural network computation. This module offers the theory and related applications of advanced deep/machine learning topics and an overview their applications to other fields, such as natural language processing, medical imaging, health, audio, and fintech etc. The deep learning algorithms which will be studied are used widely in industry by AI start-ups to AI tech giants, like, Google, Meta, Microsoft, Amazon, Tesla etc. It provides a background and related theory of deep/machine learning to manipulate data from various domains like image, video, text, audio etc. This is done by various machine learning algorithms that are discussed, implemented, and demonstrated within the module.

This module explores the major legal and regulatory issues associated with the development and us use of artificial intelligence and other technologies across various sectors, such as financial, healthcare, transportation, and military sectors. Artificial intelligence is considered as a broad discipline with the goal of creating intelligent machines that emulate and then exceed the full range of human cognition.The module will focus on various subsets of AI, such as generative AI, machine learning, unsupervised learning, and their respected legal, regulatory, and ethical challenges based on real case studies and theoretical literature. In addition to AI, the module will explore the legal and regulatory issues associated with the development and use of autonomy, privacy-preserving technologies, blockchain, and quantum computing. Autonomy is defined as the ability of a system to act independently from a human operator.The module will focus on the application of autonomy in various systems, especially in the context of autonomous weapon systems. Further, privacy preserving technologies are newer technologies such as confidential computing, federated learning, synthetic data, or homomorphic encryption that allow to compute on data while preserving fundamental principles of privacy.This module will explore the application of selected privacy-enhancing technologies in various applications, e.g. the use of federated learning in healthcare to collect and commercialise medical data from hospitals or the use of confidential computing for sensitive data sharing across various organisations. Blockchain is a special type of privacy preserving technology based on a decentralized, distributed, and often public, digital ledger which facilitates the process of recording transactions and tracking assets.The module will explore various governance models of blockchain and their legal implications. Finally, quantum technology is a class of technology that works by using the principles of quantum mechanics to gain a functionality or performance which is otherwise unattainable.The module will discuss the role of law and regulation in the current and future development and application of quantum technologies.

Module purpose:This module provides an introduction to the process of digital image formation in real and computer-generated imagery and builds on EEE1035 Programming in C. The module covers mathematical methods used to represent cameras, scene geometry and lighting in computer vision and computer graphics. The course introduces both the theoretical concepts and practical implementation of three-dimensional computer graphics used in visual effects, games and scientific visualisation. The practical implementation of computer graphics is introduced using the OpenGL libraries, which are widely used in industry. Some of the concepts developed in this module will be useful in other computer vision modules such as EEE3032: Computer Vision and Pattern Recognition. Expected prior learning: Learning equivalent to Year 1 and Year 2 Semester 1 of EE programmes.

This course introduces the core concepts of parallel computing by examining the algorithms and architectures that support it. Students will implement parallel solutions during our labs and evaluate their performance, gaining hands-on experience and insight into the challenges and potential tradeoffs involved. The course places particular emphasis on making algorithmic decisions that are informed by hardware characteristics, providing a strong foundation for designing high¿performance computing systems.

Computer networks are an essential part of almost all corporate computing facilities and even most domestic ones.  Interoperability is the key – all components must conform to the same hardware and packet specifications in order that they can be interconnected successfully.  This module introduces essential concepts about all the computer networking layering levels with some emphasis on the routing algorithms and implementation of network sensing. 

This module gives an introductory yet up-to-date description of the fundamental technologies of computational Intelligence, including evolutionary computation, neural computing and their applications. Main streams of evolutionary algorithms and meta-heuristics, including genetic algorithms, evolution strategies, genetic programming, particle swarm optimization will be taught. Basic neural network models and learning algorithms will be introduced. Interactions between evolution and learning, real-world applications to optimization and robotics, and recent advances will also be discussed. Good skill in Python programming, good knowledge in mathematics (calculus) are required.

Expected prior learning: Module EEE2041 – Computer Vision & Graphics, or equivalent learning about the geometric interpretation of Linear Algebra (e.g. homogeneous coordinates and matrices for point transformation e.g. rotation, translation, scaling). Module purpose: The module delivers a grounding in Computer Vision, suitable for students with a grounding in linear algebra similar to that provided by EEE2041 – Computer Vision & Graphics) and will help with modules such EEEM071 Advanced Topics in Computer Vision and Deep Learning. Content is presented as an application-focused tour of Computer Vision from the low-level (image processing), through to high level model fitting and object recognition.  

This module introduces general concepts of privacy enhancing technologies and aligns with key concepts recommended by the CyBoK. It will motivate the need for privacy in the modern world and touch on legal considerations, introduce concepts of transparency, control and confidentiality for privacy, and look at privacy preserving and democratic values. This module will also explore how these are realised in a range of applications.

Module purpose: Modern robotics brings together many aspects of engineering including electronics, hardware, software and AI. This leads to complex asynchronous systems that requires a systems engineering approach. The Robotics Operating System (ROS), is an extensive community built software suite that underpins most leading-edge robotics development. It provides extensive hardware interfacing and high-level functionality which allows complex systems engineering and control while abstracting away much of the complexity inherent to robotics systems design. This module will use ROS to provide a solid foundation in systems engineering based robotics.

This module will demonstrate fundamental concepts from the field of Natural Language Processing (NLP) and Computational Linguistics. It will also discuss some of the latest advances in NLP and Generative Artificial Intelligence with a focus on Language Models like BERT, T5, and GPT, and get student up to speed with current research. It will provide the necessary skills to enable students to build computational models for solving a range of problems, such as text classification, sequence classification, machine translation and building conversation agents. The students will learn how to build NLP pipelines for preparing training data and choosing appropriate algorithms and techniques to build such models. The module also focuses on aspects of ethical and trustworthy artificial intelligence with discussion on rigorous model evaluation and ethical considerations for computational modeling. Although traditional linguistic approaches will be mentioned, majority emphasis will be put on the state-of-the-art Deep Learning algorithms and Transfer Learning methods for building efficient and trustworthy NLP solutions. 

Expected prior learning: Module EEE2040 – Communications Networks or equivalent learning. Module purpose: The Internet is an important worldwide communications system; the module provides an in-depth treatment of current and evolving Internet protocols and standards, and the algorithms that underlie them. The module also permits further study on networking in modules such as EEEM018 Advanced Mobile Communication Systems, EEEM023 Network Service management and Control, EEEM032 Advanced Satellite Communication Techniques

Machine/Deep learning has emerged from computer science and artificial intelligence. It draws on methods from a variety of related subjects including statistics, applied mathematics and more specialized fields, such as pattern recognition and neural network computation. This module offers the theory and related applications of advanced deep/machine learning topics and an overview their applications to other fields, such as natural language processing, medical imaging, health, audio, and fintech etc. The deep learning algorithms which will be studied are used widely in industry by AI start-ups to AI tech giants, like, Google, Meta, Microsoft, Amazon, Tesla etc. It provides a background and related theory of deep/machine learning to manipulate data from various domains like image, video, text, audio etc. This is done by various machine learning algorithms that are discussed, implemented, and demonstrated within the module.

This module explores the major legal and regulatory issues associated with the development and us use of artificial intelligence and other technologies across various sectors, such as financial, healthcare, transportation, and military sectors. Artificial intelligence is considered as a broad discipline with the goal of creating intelligent machines that emulate and then exceed the full range of human cognition.The module will focus on various subsets of AI, such as generative AI, machine learning, unsupervised learning, and their respected legal, regulatory, and ethical challenges based on real case studies and theoretical literature. In addition to AI, the module will explore the legal and regulatory issues associated with the development and use of autonomy, privacy-preserving technologies, blockchain, and quantum computing. Autonomy is defined as the ability of a system to act independently from a human operator.The module will focus on the application of autonomy in various systems, especially in the context of autonomous weapon systems. Further, privacy preserving technologies are newer technologies such as confidential computing, federated learning, synthetic data, or homomorphic encryption that allow to compute on data while preserving fundamental principles of privacy.This module will explore the application of selected privacy-enhancing technologies in various applications, e.g. the use of federated learning in healthcare to collect and commercialise medical data from hospitals or the use of confidential computing for sensitive data sharing across various organisations. Blockchain is a special type of privacy preserving technology based on a decentralized, distributed, and often public, digital ledger which facilitates the process of recording transactions and tracking assets.The module will explore various governance models of blockchain and their legal implications. Finally, quantum technology is a class of technology that works by using the principles of quantum mechanics to gain a functionality or performance which is otherwise unattainable.The module will discuss the role of law and regulation in the current and future development and application of quantum technologies.

Computer networks are an essential part of almost all corporate computing facilities and even most domestic ones.  Interoperability is the key – all components must conform to the same hardware and packet specifications in order that they can be interconnected successfully.  This module introduces essential concepts about all the computer networking layering levels with some emphasis on the routing algorithms and implementation of network sensing. 

Module purpose:This module provides an introduction to the process of digital image formation in real and computer-generated imagery and builds on EEE1035 Programming in C. The module covers mathematical methods used to represent cameras, scene geometry and lighting in computer vision and computer graphics. The course introduces both the theoretical concepts and practical implementation of three-dimensional computer graphics used in visual effects, games and scientific visualisation. The practical implementation of computer graphics is introduced using the OpenGL libraries, which are widely used in industry. Some of the concepts developed in this module will be useful in other computer vision modules such as EEE3032: Computer Vision and Pattern Recognition. Expected prior learning: Learning equivalent to Year 1 and Year 2 Semester 1 of EE programmes.

This course introduces the core concepts of parallel computing by examining the algorithms and architectures that support it. Students will implement parallel solutions during our labs and evaluate their performance, gaining hands-on experience and insight into the challenges and potential tradeoffs involved. The course places particular emphasis on making algorithmic decisions that are informed by hardware characteristics, providing a strong foundation for designing high¿performance computing systems.

This module gives an introductory yet up-to-date description of the fundamental technologies of computational Intelligence, including evolutionary computation, neural computing and their applications. Main streams of evolutionary algorithms and meta-heuristics, including genetic algorithms, evolution strategies, genetic programming, particle swarm optimization will be taught. Basic neural network models and learning algorithms will be introduced. Interactions between evolution and learning, real-world applications to optimization and robotics, and recent advances will also be discussed. Good skill in Python programming, good knowledge in mathematics (calculus) are required.

Expected prior learning: Module EEE2041 – Computer Vision & Graphics, or equivalent learning about the geometric interpretation of Linear Algebra (e.g. homogeneous coordinates and matrices for point transformation e.g. rotation, translation, scaling). Module purpose: The module delivers a grounding in Computer Vision, suitable for students with a grounding in linear algebra similar to that provided by EEE2041 – Computer Vision & Graphics) and will help with modules such EEEM071 Advanced Topics in Computer Vision and Deep Learning. Content is presented as an application-focused tour of Computer Vision from the low-level (image processing), through to high level model fitting and object recognition.  

This module introduces general concepts of privacy enhancing technologies and aligns with key concepts recommended by the CyBoK. It will motivate the need for privacy in the modern world and touch on legal considerations, introduce concepts of transparency, control and confidentiality for privacy, and look at privacy preserving and democratic values. This module will also explore how these are realised in a range of applications.

Module purpose: Modern robotics brings together many aspects of engineering including electronics, hardware, software and AI. This leads to complex asynchronous systems that requires a systems engineering approach. The Robotics Operating System (ROS), is an extensive community built software suite that underpins most leading-edge robotics development. It provides extensive hardware interfacing and high-level functionality which allows complex systems engineering and control while abstracting away much of the complexity inherent to robotics systems design. This module will use ROS to provide a solid foundation in systems engineering based robotics.

This module will demonstrate fundamental concepts from the field of Natural Language Processing (NLP) and Computational Linguistics. It will also discuss some of the latest advances in NLP and Generative Artificial Intelligence with a focus on Language Models like BERT, T5, and GPT, and get student up to speed with current research. It will provide the necessary skills to enable students to build computational models for solving a range of problems, such as text classification, sequence classification, machine translation and building conversation agents. The students will learn how to build NLP pipelines for preparing training data and choosing appropriate algorithms and techniques to build such models. The module also focuses on aspects of ethical and trustworthy artificial intelligence with discussion on rigorous model evaluation and ethical considerations for computational modeling. Although traditional linguistic approaches will be mentioned, majority emphasis will be put on the state-of-the-art Deep Learning algorithms and Transfer Learning methods for building efficient and trustworthy NLP solutions. 

Expected prior learning: Module EEE2040 – Communications Networks or equivalent learning. Module purpose: The Internet is an important worldwide communications system; the module provides an in-depth treatment of current and evolving Internet protocols and standards, and the algorithms that underlie them. The module also permits further study on networking in modules such as EEEM018 Advanced Mobile Communication Systems, EEEM023 Network Service management and Control, EEEM032 Advanced Satellite Communication Techniques

Machine/Deep learning has emerged from computer science and artificial intelligence. It draws on methods from a variety of related subjects including statistics, applied mathematics and more specialized fields, such as pattern recognition and neural network computation. This module offers the theory and related applications of advanced deep/machine learning topics and an overview their applications to other fields, such as natural language processing, medical imaging, health, audio, and fintech etc. The deep learning algorithms which will be studied are used widely in industry by AI start-ups to AI tech giants, like, Google, Meta, Microsoft, Amazon, Tesla etc. It provides a background and related theory of deep/machine learning to manipulate data from various domains like image, video, text, audio etc. This is done by various machine learning algorithms that are discussed, implemented, and demonstrated within the module.

This module explores the major legal and regulatory issues associated with the development and us use of artificial intelligence and other technologies across various sectors, such as financial, healthcare, transportation, and military sectors. Artificial intelligence is considered as a broad discipline with the goal of creating intelligent machines that emulate and then exceed the full range of human cognition.The module will focus on various subsets of AI, such as generative AI, machine learning, unsupervised learning, and their respected legal, regulatory, and ethical challenges based on real case studies and theoretical literature. In addition to AI, the module will explore the legal and regulatory issues associated with the development and use of autonomy, privacy-preserving technologies, blockchain, and quantum computing. Autonomy is defined as the ability of a system to act independently from a human operator.The module will focus on the application of autonomy in various systems, especially in the context of autonomous weapon systems. Further, privacy preserving technologies are newer technologies such as confidential computing, federated learning, synthetic data, or homomorphic encryption that allow to compute on data while preserving fundamental principles of privacy.This module will explore the application of selected privacy-enhancing technologies in various applications, e.g. the use of federated learning in healthcare to collect and commercialise medical data from hospitals or the use of confidential computing for sensitive data sharing across various organisations. Blockchain is a special type of privacy preserving technology based on a decentralized, distributed, and often public, digital ledger which facilitates the process of recording transactions and tracking assets.The module will explore various governance models of blockchain and their legal implications. Finally, quantum technology is a class of technology that works by using the principles of quantum mechanics to gain a functionality or performance which is otherwise unattainable.The module will discuss the role of law and regulation in the current and future development and application of quantum technologies.

This module introduces the fundamental concepts and technologies underlying virtual and augmented reality (VR/AR) and the emerging concept of the Metaverse. It also examines current and future technological challenges and explores the potential impact of these technologies on industry and wider society.

Medical robotics is a rapidly developing industry that is vital to healthcare systems seeking better outcomes at reduced overall cost. This module introduces students to the workings of robots and how this is applied in solving both surgical and broader healthcare challenges. Students learn through lectures, practical sessions, tutorials and seminars requiring engagement with scientific literature. Case studies are a critical part of the module. These are industrially or research-based and require students to think about clinical conditions, ethical considerations and a range of attitudes to healthcare.The unit of assessment requires students to develop an understanding of the general theory behind robotics and identify the particularities applying to medical robots. The unit of assessment will also address the wider issues associated with medical robotics and their application. Practical sessions enable students to work with each other to solve problems and demonstrate the theoretical components of the module. An overarching series of lectures delivers critical technical information and links students¿ learning with case-studies to illustrate mechanical and medical issues. Seminars complement lectures and are designed for students to critically analyse, discuss and focus on particular aspects of medical robotic research in the scientific literature.

Module purpose: Advances related to energy efficiency issues and cost reductions have resulted in the rapid growth and deployment of networked devices and sensing/actuation systems that connect the physical world with the cyber world. This evolving framework, now recognized as the Artificial Intelligence of Things (AIoT), integrates IoT technologies with artificial intelligence (AI) to enhance automation, intelligence, and decision-making. AIoT incorporates several cutting-edge technologies, including wireless sensor networks, pervasive systems, ambient intelligence, context awareness, distributed systems, and machine learning-driven analytics. Module Overview: The advanced AIoT module is designed to provide a comprehensive understanding of how machine communications , coupled with AI, contribute to creating smart artificial intelligence-driven environments, focusing on networking and communication systems. The module provides an overview of the key concepts and enabling technologies for AIoT. It encompasses a cross-layer approach, allowing students to explore the practical aspects of sensors, actuators, and mainly communication systems for AIoT across physical, media access, and network layers. This includes security considerations, satellite AIoT, positioning and tracking for industrial applications, AIoT Platforms (Hardware, Software), protocols and standards (e.g. 6LowPAN, ZigBee, CoAp), semantic technologies, and data and information processing mechanisms. Also, the module explores AI-driven methodologies such as ensemble learning, multi-armed bandits for sequential decision-making, and lightweight machine learning Mmodels for AIoT applications. These techniques enhance real-time data analysis, predictive maintenance, anomaly detection, and adaptive control while ensuring computational efficiency.

This module offers an introduction to the use of AI in society, work, media and communication, government and policy. It puts people ¿ as opposed to technology -- in the centre of AI, and highlights core considerations in planning for AI applications as a response to issues and considerations in the society. This is done by exploring varied positions of users and stakeholders in relation to AI, examining the suitability of AI and associated tools and methods to the productivity/progress/propagation in society. In this module we discuss the opportunities, but also the challenges, risks, threats and ethical implications involved in the use of AI in society.

The need for computational power and data storage continues to drive demand for more highly capable systems. Highly data intensive applications demand fast access to terabytes, petabytes, even exabytes of storage; processor intensive applications demand access to various types of processors in various configurations. Such applications are increasingly being developed in both scientific and industrial contexts and need to be variously scalable and supportable for large numbers of geographically distributed users. This module will provide insights into how Cloud Computing attempts to meet the varying needs of such applications.

The module provides an application-focused tour of machine learning for real-world healthcare research and application from understanding various healthcare components, ethical concerns to pre-processing and analysing healthcare data for classification, survival and risk analysis, and early prediction tasks. The module requires and builds on the knowledge of basic machine learning, linear algebra, and familiarity with Python programming.   Labs are designed to support understanding of the theory and enable development of practical skills required for future employability.

Recently, Artificial Intelligence (AI) has been playing a key role in the research and development of scientific and technological breakthroughs in many disciplines to solve real world problems, providing new foundations and steppingstones to foster more advances and solutions. In this context, AI has a great potential to play a transformative role in helping to achieve the United Nations Sustainable Development Goals (UNSDG), by providing new insights, enabling more efficient use of resources, and supporting a better understanding of complex systems that underpin the dynamics of people's lives and the planet's environment. Therefore, the purpose of this module is to present the key concepts with practical applications related to the development of more sustainable AI techniques (e.g. model, data and energy efficiency, bias and unfairness identification and mitigation, trustworthy AI, physics-informed neural networks etc.), and AI solutions to support UNSDGs (e.g. clean air, clean energy, clean water, waste management, smart manufacturing etc.).

The module introduces general information and network security principles, challenges and goals and then focuses on main security mechanisms and protocols for protecting network communication across different layers of the Internet protocol stack. This will include discussion on various attacks on the networks, penetration testing tools and possible countermeasures to ensure protection of authentication, confidentiality and end-to-end security of communications. In labs students will be able to practice experience with various network security protocols and tools.

The main application of machine learning is out-of-sample prediction. Prediction accuracy is typically evaluated in terms of squared error, where the error is difference between the prediction and the actual realization. In certain situations, such as forecasts of inflation or output from the Bank of England, an accurate prediction is enough. However, there are situations, like policy evaluation, in which we care about causal effects. Suppose that the Secretary of Education introduces three additional hours of mathematics in primary school to increase student GSE scores. Here the objective is to isolate the effect of additional hours of math on GSE score. In general, for each pupil, we have a lot of individual characteristics, which we need to control for. Data reduction techniques, such as LASSO, regression tree, random forest, help to eliminate all irrelevant information so that we can isolate the effect of the policy. This module is structured in two parts. In the first part, we review econometric tools for policy evaluation, such as instrumental variables, panel data, difference-in-difference, synthetic control, regression discontinuity. In the second part, we look at the same techniques when many instruments are available or many additional control variables are available.

Computer networks are an essential part of almost all corporate computing facilities and even most domestic ones.  Interoperability is the key – all components must conform to the same hardware and packet specifications in order that they can be interconnected successfully.  This module introduces essential concepts about all the computer networking layering levels with some emphasis on the routing algorithms and implementation of network sensing. 

Module purpose:This module provides an introduction to the process of digital image formation in real and computer-generated imagery and builds on EEE1035 Programming in C. The module covers mathematical methods used to represent cameras, scene geometry and lighting in computer vision and computer graphics. The course introduces both the theoretical concepts and practical implementation of three-dimensional computer graphics used in visual effects, games and scientific visualisation. The practical implementation of computer graphics is introduced using the OpenGL libraries, which are widely used in industry. Some of the concepts developed in this module will be useful in other computer vision modules such as EEE3032: Computer Vision and Pattern Recognition. Expected prior learning: Learning equivalent to Year 1 and Year 2 Semester 1 of EE programmes.

This course introduces the core concepts of parallel computing by examining the algorithms and architectures that support it. Students will implement parallel solutions during our labs and evaluate their performance, gaining hands-on experience and insight into the challenges and potential tradeoffs involved. The course places particular emphasis on making algorithmic decisions that are informed by hardware characteristics, providing a strong foundation for designing high¿performance computing systems.

This module gives an introductory yet up-to-date description of the fundamental technologies of computational Intelligence, including evolutionary computation, neural computing and their applications. Main streams of evolutionary algorithms and meta-heuristics, including genetic algorithms, evolution strategies, genetic programming, particle swarm optimization will be taught. Basic neural network models and learning algorithms will be introduced. Interactions between evolution and learning, real-world applications to optimization and robotics, and recent advances will also be discussed. Good skill in Python programming, good knowledge in mathematics (calculus) are required.

Expected prior learning: Module EEE2041 – Computer Vision & Graphics, or equivalent learning about the geometric interpretation of Linear Algebra (e.g. homogeneous coordinates and matrices for point transformation e.g. rotation, translation, scaling). Module purpose: The module delivers a grounding in Computer Vision, suitable for students with a grounding in linear algebra similar to that provided by EEE2041 – Computer Vision & Graphics) and will help with modules such EEEM071 Advanced Topics in Computer Vision and Deep Learning. Content is presented as an application-focused tour of Computer Vision from the low-level (image processing), through to high level model fitting and object recognition.  

This module introduces general concepts of privacy enhancing technologies and aligns with key concepts recommended by the CyBoK. It will motivate the need for privacy in the modern world and touch on legal considerations, introduce concepts of transparency, control and confidentiality for privacy, and look at privacy preserving and democratic values. This module will also explore how these are realised in a range of applications.

Module purpose: Modern robotics brings together many aspects of engineering including electronics, hardware, software and AI. This leads to complex asynchronous systems that requires a systems engineering approach. The Robotics Operating System (ROS), is an extensive community built software suite that underpins most leading-edge robotics development. It provides extensive hardware interfacing and high-level functionality which allows complex systems engineering and control while abstracting away much of the complexity inherent to robotics systems design. This module will use ROS to provide a solid foundation in systems engineering based robotics.

This module will demonstrate fundamental concepts from the field of Natural Language Processing (NLP) and Computational Linguistics. It will also discuss some of the latest advances in NLP and Generative Artificial Intelligence with a focus on Language Models like BERT, T5, and GPT, and get student up to speed with current research. It will provide the necessary skills to enable students to build computational models for solving a range of problems, such as text classification, sequence classification, machine translation and building conversation agents. The students will learn how to build NLP pipelines for preparing training data and choosing appropriate algorithms and techniques to build such models. The module also focuses on aspects of ethical and trustworthy artificial intelligence with discussion on rigorous model evaluation and ethical considerations for computational modeling. Although traditional linguistic approaches will be mentioned, majority emphasis will be put on the state-of-the-art Deep Learning algorithms and Transfer Learning methods for building efficient and trustworthy NLP solutions. 

Expected prior learning: Module EEE2040 – Communications Networks or equivalent learning. Module purpose: The Internet is an important worldwide communications system; the module provides an in-depth treatment of current and evolving Internet protocols and standards, and the algorithms that underlie them. The module also permits further study on networking in modules such as EEEM018 Advanced Mobile Communication Systems, EEEM023 Network Service management and Control, EEEM032 Advanced Satellite Communication Techniques

Machine/Deep learning has emerged from computer science and artificial intelligence. It draws on methods from a variety of related subjects including statistics, applied mathematics and more specialized fields, such as pattern recognition and neural network computation. This module offers the theory and related applications of advanced deep/machine learning topics and an overview their applications to other fields, such as natural language processing, medical imaging, health, audio, and fintech etc. The deep learning algorithms which will be studied are used widely in industry by AI start-ups to AI tech giants, like, Google, Meta, Microsoft, Amazon, Tesla etc. It provides a background and related theory of deep/machine learning to manipulate data from various domains like image, video, text, audio etc. This is done by various machine learning algorithms that are discussed, implemented, and demonstrated within the module.

This module explores the major legal and regulatory issues associated with the development and us use of artificial intelligence and other technologies across various sectors, such as financial, healthcare, transportation, and military sectors. Artificial intelligence is considered as a broad discipline with the goal of creating intelligent machines that emulate and then exceed the full range of human cognition.The module will focus on various subsets of AI, such as generative AI, machine learning, unsupervised learning, and their respected legal, regulatory, and ethical challenges based on real case studies and theoretical literature. In addition to AI, the module will explore the legal and regulatory issues associated with the development and use of autonomy, privacy-preserving technologies, blockchain, and quantum computing. Autonomy is defined as the ability of a system to act independently from a human operator.The module will focus on the application of autonomy in various systems, especially in the context of autonomous weapon systems. Further, privacy preserving technologies are newer technologies such as confidential computing, federated learning, synthetic data, or homomorphic encryption that allow to compute on data while preserving fundamental principles of privacy.This module will explore the application of selected privacy-enhancing technologies in various applications, e.g. the use of federated learning in healthcare to collect and commercialise medical data from hospitals or the use of confidential computing for sensitive data sharing across various organisations. Blockchain is a special type of privacy preserving technology based on a decentralized, distributed, and often public, digital ledger which facilitates the process of recording transactions and tracking assets.The module will explore various governance models of blockchain and their legal implications. Finally, quantum technology is a class of technology that works by using the principles of quantum mechanics to gain a functionality or performance which is otherwise unattainable.The module will discuss the role of law and regulation in the current and future development and application of quantum technologies.

This module introduces the fundamental concepts and technologies underlying virtual and augmented reality (VR/AR) and the emerging concept of the Metaverse. It also examines current and future technological challenges and explores the potential impact of these technologies on industry and wider society.

Medical robotics is a rapidly developing industry that is vital to healthcare systems seeking better outcomes at reduced overall cost. This module introduces students to the workings of robots and how this is applied in solving both surgical and broader healthcare challenges. Students learn through lectures, practical sessions, tutorials and seminars requiring engagement with scientific literature. Case studies are a critical part of the module. These are industrially or research-based and require students to think about clinical conditions, ethical considerations and a range of attitudes to healthcare.The unit of assessment requires students to develop an understanding of the general theory behind robotics and identify the particularities applying to medical robots. The unit of assessment will also address the wider issues associated with medical robotics and their application. Practical sessions enable students to work with each other to solve problems and demonstrate the theoretical components of the module. An overarching series of lectures delivers critical technical information and links students¿ learning with case-studies to illustrate mechanical and medical issues. Seminars complement lectures and are designed for students to critically analyse, discuss and focus on particular aspects of medical robotic research in the scientific literature.

Module purpose: Advances related to energy efficiency issues and cost reductions have resulted in the rapid growth and deployment of networked devices and sensing/actuation systems that connect the physical world with the cyber world. This evolving framework, now recognized as the Artificial Intelligence of Things (AIoT), integrates IoT technologies with artificial intelligence (AI) to enhance automation, intelligence, and decision-making. AIoT incorporates several cutting-edge technologies, including wireless sensor networks, pervasive systems, ambient intelligence, context awareness, distributed systems, and machine learning-driven analytics. Module Overview: The advanced AIoT module is designed to provide a comprehensive understanding of how machine communications , coupled with AI, contribute to creating smart artificial intelligence-driven environments, focusing on networking and communication systems. The module provides an overview of the key concepts and enabling technologies for AIoT. It encompasses a cross-layer approach, allowing students to explore the practical aspects of sensors, actuators, and mainly communication systems for AIoT across physical, media access, and network layers. This includes security considerations, satellite AIoT, positioning and tracking for industrial applications, AIoT Platforms (Hardware, Software), protocols and standards (e.g. 6LowPAN, ZigBee, CoAp), semantic technologies, and data and information processing mechanisms. Also, the module explores AI-driven methodologies such as ensemble learning, multi-armed bandits for sequential decision-making, and lightweight machine learning Mmodels for AIoT applications. These techniques enhance real-time data analysis, predictive maintenance, anomaly detection, and adaptive control while ensuring computational efficiency.

This module offers an introduction to the use of AI in society, work, media and communication, government and policy. It puts people ¿ as opposed to technology -- in the centre of AI, and highlights core considerations in planning for AI applications as a response to issues and considerations in the society. This is done by exploring varied positions of users and stakeholders in relation to AI, examining the suitability of AI and associated tools and methods to the productivity/progress/propagation in society. In this module we discuss the opportunities, but also the challenges, risks, threats and ethical implications involved in the use of AI in society.

The need for computational power and data storage continues to drive demand for more highly capable systems. Highly data intensive applications demand fast access to terabytes, petabytes, even exabytes of storage; processor intensive applications demand access to various types of processors in various configurations. Such applications are increasingly being developed in both scientific and industrial contexts and need to be variously scalable and supportable for large numbers of geographically distributed users. This module will provide insights into how Cloud Computing attempts to meet the varying needs of such applications.

The module provides an application-focused tour of machine learning for real-world healthcare research and application from understanding various healthcare components, ethical concerns to pre-processing and analysing healthcare data for classification, survival and risk analysis, and early prediction tasks. The module requires and builds on the knowledge of basic machine learning, linear algebra, and familiarity with Python programming.   Labs are designed to support understanding of the theory and enable development of practical skills required for future employability.

Recently, Artificial Intelligence (AI) has been playing a key role in the research and development of scientific and technological breakthroughs in many disciplines to solve real world problems, providing new foundations and steppingstones to foster more advances and solutions. In this context, AI has a great potential to play a transformative role in helping to achieve the United Nations Sustainable Development Goals (UNSDG), by providing new insights, enabling more efficient use of resources, and supporting a better understanding of complex systems that underpin the dynamics of people's lives and the planet's environment. Therefore, the purpose of this module is to present the key concepts with practical applications related to the development of more sustainable AI techniques (e.g. model, data and energy efficiency, bias and unfairness identification and mitigation, trustworthy AI, physics-informed neural networks etc.), and AI solutions to support UNSDGs (e.g. clean air, clean energy, clean water, waste management, smart manufacturing etc.).

The module introduces general information and network security principles, challenges and goals and then focuses on main security mechanisms and protocols for protecting network communication across different layers of the Internet protocol stack. This will include discussion on various attacks on the networks, penetration testing tools and possible countermeasures to ensure protection of authentication, confidentiality and end-to-end security of communications. In labs students will be able to practice experience with various network security protocols and tools.

The main application of machine learning is out-of-sample prediction. Prediction accuracy is typically evaluated in terms of squared error, where the error is difference between the prediction and the actual realization. In certain situations, such as forecasts of inflation or output from the Bank of England, an accurate prediction is enough. However, there are situations, like policy evaluation, in which we care about causal effects. Suppose that the Secretary of Education introduces three additional hours of mathematics in primary school to increase student GSE scores. Here the objective is to isolate the effect of additional hours of math on GSE score. In general, for each pupil, we have a lot of individual characteristics, which we need to control for. Data reduction techniques, such as LASSO, regression tree, random forest, help to eliminate all irrelevant information so that we can isolate the effect of the policy. This module is structured in two parts. In the first part, we review econometric tools for policy evaluation, such as instrumental variables, panel data, difference-in-difference, synthetic control, regression discontinuity. In the second part, we look at the same techniques when many instruments are available or many additional control variables are available.