BSc (Hons) or MEng — 2027 entry Computer Science

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. The resulting framework, known as the Internet of Things (IoT), incorporates several technologies, including wireless sensor networks, pervasive systems, ambient intelligence, context awareness, and distributed systems. The IoT module is designed to provide a comprehensive understanding of how machine communications contribute to creating smart, artificial intelligence-driven environments focusing on software networking and communication systems. The module provides an overview of the key concepts and enabling technologies for the Internet of Things. It encompasses a cross-layer approach, allowing students to explore the practical aspects of sensors, actuators, and mainly communication systems for IoT across physical, media access, and network layers. This includes security considerations, satellite IoT, positioning and tracking for industrial applications, IoT Platforms (Hardware, Software), protocols and standards (e.g., 6LowPAN, ZigBee, CoAp), semantic technologies, and data and information processing mechanisms. Also, the module seamlessly integrates cutting-edge machine learning techniques tailored for IoT applications, ensuring optimal performance and adaptability.

A distributed system is built of a network of computing devices, which coordinate their actions via message passing to achieve a common goal. Typically, system components are assumed to operate concurrently, lack a global clock and to fail independently. This module shall build and consolidate your knowledge of advanced algorithms, computer networking and/or parallel computing. You shall learn to describe the principles of distributed systems, such as communication, scheduling and fault tolerance as well as to compare and contrast between a broad set of instances of distributed systems, algorithms and protocols (e.g., peer-to-peer systems, distributed hash tables, MixNets, distributed ledger technologies) within the context of various applications.    

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.

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 examines the architectural/design needs and challenges encountered when developing and deploying secure, resilient and scalable web applications using the latest technology. It also provides an introduction to approaches used in modern Internet-scale web applications, covering technologies used in and/or developed by familiar companies such as Amazon, Netflix and Google.

This module delivers an applied perspective on international business strategy, providing students with the necessary knowledge to comprehend the principles of International Business Strategy and its practical application to Emerging Markets. Participants will gain insight into groups such as BRICS, E7, MINT, Next Eleven (N-11), and frontier markets. The curriculum offers a comprehensive framework for analysing international business strategy as an interdisciplinary endeavor within the context of global and competitive markets, thereby informing corporate decision-making. It explores the distinctions between corporate structures in Emerging Markets and those in developed economies, emphasising that successful implementation of international strategies often requires tailored approaches.Students will examine the process by which companies from Emerging Markets expand internationally, investigating both their success and failures. Furthermore, the module addresses contemporary issues relevant to international business strategy, including change management, technology strategy, sustainability initiatives, and Non-Market Strategy. The module is structured around three principal themes: Define international business strategy.Examining the international business strategy as applied to Emerging Markets.Explore how international business strategies are developed and implemented during periods of global and internal challenges.Through theoretical and practical exploration, students will understand how business strategy can be formulated in response to environmental and internal factors

In recent years, AI has seen tremendous growth due in large part to more powerful computers, larger scale data and techniques to establish comprehensive framework through deeper neural networks. This module introduces a wide range of deep learning and the latest state of art techniques in AI for serving the world through innovation, understanding and compassion. Fundamental concepts on applied maths and establishment on effective learning objectives that thread through key elements in machine learning techniques will be discussed throughout the module. Students will study how to build suitable AI systems that can operate in complicated, real-world environments. The module also prepares students to explore further challenges and opportunities to work with advanced AI and bring them to new frontiers. The module content will typically be updated each year reflecting the latest evolution in AI.  

The course is an introduction in formal methods for system specification and verification. It will focus on logic-based formalisms and techniques, and specifically on model checking. The main logics taught will be temporal logics, which are mainstream  in verification, especially analysis of hardware systems. Other logics and verification techniques (such as theorem proving) will be included to a smaller extended. Model checkers will be used in the labs, on different system-verification problems. Elements of building model checking tools will be presented and explored. Some elements of advanced verification techniques (e.g., abstraction) will be mentioned.

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

In an era where data is a key driver of innovation and growth, practical expertise in data science is essential for solving complex business challenges. This module uses a case study-based approach to teach data science concepts and techniques, providing students with real-world contexts to develop their skills. Each case study focuses on a different industry or application, such as healthcare, finance, telecommunication, social media, and marketing, allowing students to explore the diverse ways data science creates value. Through hands-on experience with tools, methods, and datasets, students will gain practical insights into solving problems, generating predictions, and making data-driven decisions.

This module introduces students to the techniques and tools to discover known vulnerabilities in systems and applications, and use appropriate techniques to carry out attacks. A practical project-based assessment allows students to demonstrate their ability to successfully exploit vulnerable systems to exfiltrate data and circumvent access controls. The module will introduce students to the legal and ethical considerations of ethical hacking, as well as the processes by which vulnerabilities are reported, classified, documented, and mitigated by security practitioners.

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. The resulting framework, known as the Internet of Things (IoT), incorporates several technologies, including wireless sensor networks, pervasive systems, ambient intelligence, context awareness, and distributed systems. The IoT module is designed to provide a comprehensive understanding of how machine communications contribute to creating smart, artificial intelligence-driven environments focusing on software networking and communication systems. The module provides an overview of the key concepts and enabling technologies for the Internet of Things. It encompasses a cross-layer approach, allowing students to explore the practical aspects of sensors, actuators, and mainly communication systems for IoT across physical, media access, and network layers. This includes security considerations, satellite IoT, positioning and tracking for industrial applications, IoT Platforms (Hardware, Software), protocols and standards (e.g., 6LowPAN, ZigBee, CoAp), semantic technologies, and data and information processing mechanisms. Also, the module seamlessly integrates cutting-edge machine learning techniques tailored for IoT applications, ensuring optimal performance and adaptability.

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.

A distributed system is built of a network of computing devices, which coordinate their actions via message passing to achieve a common goal. Typically, system components are assumed to operate concurrently, lack a global clock and to fail independently. This module shall build and consolidate your knowledge of advanced algorithms, computer networking and/or parallel computing. You shall learn to describe the principles of distributed systems, such as communication, scheduling and fault tolerance as well as to compare and contrast between a broad set of instances of distributed systems, algorithms and protocols (e.g., peer-to-peer systems, distributed hash tables, MixNets, distributed ledger technologies) within the context of various applications.    

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 examines the architectural/design needs and challenges encountered when developing and deploying secure, resilient and scalable web applications using the latest technology. It also provides an introduction to approaches used in modern Internet-scale web applications, covering technologies used in and/or developed by familiar companies such as Amazon, Netflix and Google.

This module delivers an applied perspective on international business strategy, providing students with the necessary knowledge to comprehend the principles of International Business Strategy and its practical application to Emerging Markets. Participants will gain insight into groups such as BRICS, E7, MINT, Next Eleven (N-11), and frontier markets. The curriculum offers a comprehensive framework for analysing international business strategy as an interdisciplinary endeavor within the context of global and competitive markets, thereby informing corporate decision-making. It explores the distinctions between corporate structures in Emerging Markets and those in developed economies, emphasising that successful implementation of international strategies often requires tailored approaches.Students will examine the process by which companies from Emerging Markets expand internationally, investigating both their success and failures. Furthermore, the module addresses contemporary issues relevant to international business strategy, including change management, technology strategy, sustainability initiatives, and Non-Market Strategy. The module is structured around three principal themes: Define international business strategy.Examining the international business strategy as applied to Emerging Markets.Explore how international business strategies are developed and implemented during periods of global and internal challenges.Through theoretical and practical exploration, students will understand how business strategy can be formulated in response to environmental and internal factors

In recent years, AI has seen tremendous growth due in large part to more powerful computers, larger scale data and techniques to establish comprehensive framework through deeper neural networks. This module introduces a wide range of deep learning and the latest state of art techniques in AI for serving the world through innovation, understanding and compassion. Fundamental concepts on applied maths and establishment on effective learning objectives that thread through key elements in machine learning techniques will be discussed throughout the module. Students will study how to build suitable AI systems that can operate in complicated, real-world environments. The module also prepares students to explore further challenges and opportunities to work with advanced AI and bring them to new frontiers. The module content will typically be updated each year reflecting the latest evolution in AI.  

The course is an introduction in formal methods for system specification and verification. It will focus on logic-based formalisms and techniques, and specifically on model checking. The main logics taught will be temporal logics, which are mainstream  in verification, especially analysis of hardware systems. Other logics and verification techniques (such as theorem proving) will be included to a smaller extended. Model checkers will be used in the labs, on different system-verification problems. Elements of building model checking tools will be presented and explored. Some elements of advanced verification techniques (e.g., abstraction) will be mentioned.

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

In an era where data is a key driver of innovation and growth, practical expertise in data science is essential for solving complex business challenges. This module uses a case study-based approach to teach data science concepts and techniques, providing students with real-world contexts to develop their skills. Each case study focuses on a different industry or application, such as healthcare, finance, telecommunication, social media, and marketing, allowing students to explore the diverse ways data science creates value. Through hands-on experience with tools, methods, and datasets, students will gain practical insights into solving problems, generating predictions, and making data-driven decisions.

This module introduces students to the techniques and tools to discover known vulnerabilities in systems and applications, and use appropriate techniques to carry out attacks. A practical project-based assessment allows students to demonstrate their ability to successfully exploit vulnerable systems to exfiltrate data and circumvent access controls. The module will introduce students to the legal and ethical considerations of ethical hacking, as well as the processes by which vulnerabilities are reported, classified, documented, and mitigated by security practitioners.

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.

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. The resulting framework, known as the Internet of Things (IoT), incorporates several technologies, including wireless sensor networks, pervasive systems, ambient intelligence, context awareness, and distributed systems. The IoT module is designed to provide a comprehensive understanding of how machine communications contribute to creating smart, artificial intelligence-driven environments focusing on software networking and communication systems. The module provides an overview of the key concepts and enabling technologies for the Internet of Things. It encompasses a cross-layer approach, allowing students to explore the practical aspects of sensors, actuators, and mainly communication systems for IoT across physical, media access, and network layers. This includes security considerations, satellite IoT, positioning and tracking for industrial applications, IoT Platforms (Hardware, Software), protocols and standards (e.g., 6LowPAN, ZigBee, CoAp), semantic technologies, and data and information processing mechanisms. Also, the module seamlessly integrates cutting-edge machine learning techniques tailored for IoT applications, ensuring optimal performance and adaptability.

A distributed system is built of a network of computing devices, which coordinate their actions via message passing to achieve a common goal. Typically, system components are assumed to operate concurrently, lack a global clock and to fail independently. This module shall build and consolidate your knowledge of advanced algorithms, computer networking and/or parallel computing. You shall learn to describe the principles of distributed systems, such as communication, scheduling and fault tolerance as well as to compare and contrast between a broad set of instances of distributed systems, algorithms and protocols (e.g., peer-to-peer systems, distributed hash tables, MixNets, distributed ledger technologies) within the context of various applications.    

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 examines the architectural/design needs and challenges encountered when developing and deploying secure, resilient and scalable web applications using the latest technology. It also provides an introduction to approaches used in modern Internet-scale web applications, covering technologies used in and/or developed by familiar companies such as Amazon, Netflix and Google.

This module delivers an applied perspective on international business strategy, providing students with the necessary knowledge to comprehend the principles of International Business Strategy and its practical application to Emerging Markets. Participants will gain insight into groups such as BRICS, E7, MINT, Next Eleven (N-11), and frontier markets. The curriculum offers a comprehensive framework for analysing international business strategy as an interdisciplinary endeavor within the context of global and competitive markets, thereby informing corporate decision-making. It explores the distinctions between corporate structures in Emerging Markets and those in developed economies, emphasising that successful implementation of international strategies often requires tailored approaches.Students will examine the process by which companies from Emerging Markets expand internationally, investigating both their success and failures. Furthermore, the module addresses contemporary issues relevant to international business strategy, including change management, technology strategy, sustainability initiatives, and Non-Market Strategy. The module is structured around three principal themes: Define international business strategy.Examining the international business strategy as applied to Emerging Markets.Explore how international business strategies are developed and implemented during periods of global and internal challenges.Through theoretical and practical exploration, students will understand how business strategy can be formulated in response to environmental and internal factors

In recent years, AI has seen tremendous growth due in large part to more powerful computers, larger scale data and techniques to establish comprehensive framework through deeper neural networks. This module introduces a wide range of deep learning and the latest state of art techniques in AI for serving the world through innovation, understanding and compassion. Fundamental concepts on applied maths and establishment on effective learning objectives that thread through key elements in machine learning techniques will be discussed throughout the module. Students will study how to build suitable AI systems that can operate in complicated, real-world environments. The module also prepares students to explore further challenges and opportunities to work with advanced AI and bring them to new frontiers. The module content will typically be updated each year reflecting the latest evolution in AI.  

The course is an introduction in formal methods for system specification and verification. It will focus on logic-based formalisms and techniques, and specifically on model checking. The main logics taught will be temporal logics, which are mainstream  in verification, especially analysis of hardware systems. Other logics and verification techniques (such as theorem proving) will be included to a smaller extended. Model checkers will be used in the labs, on different system-verification problems. Elements of building model checking tools will be presented and explored. Some elements of advanced verification techniques (e.g., abstraction) will be mentioned.

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

In an era where data is a key driver of innovation and growth, practical expertise in data science is essential for solving complex business challenges. This module uses a case study-based approach to teach data science concepts and techniques, providing students with real-world contexts to develop their skills. Each case study focuses on a different industry or application, such as healthcare, finance, telecommunication, social media, and marketing, allowing students to explore the diverse ways data science creates value. Through hands-on experience with tools, methods, and datasets, students will gain practical insights into solving problems, generating predictions, and making data-driven decisions.

This module introduces students to the techniques and tools to discover known vulnerabilities in systems and applications, and use appropriate techniques to carry out attacks. A practical project-based assessment allows students to demonstrate their ability to successfully exploit vulnerable systems to exfiltrate data and circumvent access controls. The module will introduce students to the legal and ethical considerations of ethical hacking, as well as the processes by which vulnerabilities are reported, classified, documented, and mitigated by security practitioners.

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.

Expected prior learning:  Module EEE3008–Fundamentals of Digital Signal Processing  or equivalent learning about signal processing. Module purpose:  The module discusses basic concepts, signal processing methods and human computer interaction applications of speech processing and recognition including auditory perception and psychoacoustics. You will be taught how to extract salient features from speech signals, how to design a model of spoken language, how to perform recognition and training, and given an insight into current research on spontaneous speech recognition, such as speaker adaptation and solutions for robustness to noise. Demonstrations, interesting illustrations and working examples will be given. Successful students can either proceed to do PhDs or get jobs in the R & D departments of industry, i.e. jobs that are at a higher level than mere software package operators. The presented techniques have many other applications beyond speech, including expert systems and financial modelling. Module EEEM030 contributes to the development of student’s knowledge in audio and speech processing and recognition, which may be useful for their taking of other modules such as: EEEM071 Advanced Topics in Computer Vision and Deep Learning EEEM004 60 Credit Standard Project EEEM005 AI and AI Programming EEEM066 Fundamentals of Machine Learning EEEM067 AR, VR and Metaverse EEEM068 Applied Machine Learning Module EEEM030 contributes to student knowledge in audio and speech processing and thus useful for students taking 60 credit project (EEEM004) related to audio and speech processing and recognition. EEEM030 is related to EEEM005, EEEM066 and EEEM068 due to the fact that machine learning/AI techniques are used for speech and speaker recognition, therefore, EEEM030 contributes to the development of student knowledge in machine learning/AI, which is beneficial for their taking of machine learning and AI related modules such as EEEM005, EEEM066 and EEEM068. One of the applications of audio and speech processing is to apply it to AR, VR and Metaverse for spatial sound production and reproduction which is a key enabling technology for AR, VR, and Metaverse, for virtual sound reproduction. Therefore, knowledge gained from EEEM030 would be useful for the taking of the module EEEM067. Module EEE030 also benefits from knowledge gained from other modules such as: EEE3008 Digital Signal Processing EEE1033 Computer and Digital Logic EEE1035 Programming  in C EEE3042 Audio and Video Processing EEE3032 Computer Vision and Pattern Recognition The modules EEE1033 and EEE1035 provide students with some useful skills in programming, which will be beneficial for them to complete the computer programming based coursework components, by turning the signal processing theories and methods into working program codes. The module EEE3008 provides students with knowledge and skills on fundamental digital signal processing skills which are essential in understanding the application of these skills to audio and speech data. EEE3042 covers both audio and video processing and coding, and the audio related materials are highly relevant and thus useful for the EEEM030 module. The pattern recognition skills gained from EEE3032 would be useful for understanding the use of pattern recognition algorithms to speech data for achieving speech and speaker recognition.  

The module introduces general cryptographic concepts, challenges and goals and then focuses on foundational cryptographic primitives and algorithms in the fields of symmetric (a.k.a. private-key) and asymmetric (a.k.a. public-key) cryptography. The module will introduce mathematical foundations that are essential for the functionality and security of cryptographic algorithms and then focus on the security definitions and constructions of concrete algorithms for authentication, confidentiality and integrity. In labs students will develop and test their understanding of the material and learn how to implement and use existing algorithms from symmetric and asymmetric cryptography.

This module provides knowledge about advanced digital circuit design and the hardware description language VHDL. The practical part of the course is concerned with FPGA implementation, using modern CAD tools and FPGA prototyping boards. This module builds on from many Electronic Engineering modules at undergraduate level in the topics of Circuit Design and Processor design such EEE2045 Electrical Science II and will lead on to EEEM059 Space Avionics

Quantum computing exploits the principles of superposition and entanglement to perform certain computational tasks more efficiently than is possible using classical computers. While quantum computing relies on quantum physics, it is possible to understand and apply quantum computation without detailed prior training in quantum mechanics. This module provides an introduction to quantum computing from a computational and algorithmic perspective. It introduces the mathematical framework used to describe qubits and quantum logic, develops the concept of quantum circuits, and explores how quantum algorithms may be applied to optimization problems of practical relevance. The module emphasises how quantum information is processed, rather than how quantum hardware is physically realised. This module is suitable for students from Physics, Computer Science, Electronic Engineering, and related disciplines.

Module purpose: This course offers an introduction to image processing and computer vision for those interested in the science and technology of machine vision. It provides background and the theory for building artificial systems that manipulate videos and images and alter or analyse their information content. This is done by various computer algorithms that are discussed, implemented and demonstrated.

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.

This module covers circuit-based quantum computers and some of the algorithms that can be implemented using them.  The idea of a classical computational algorithm and its complexity is introduced, and used as a language to discuss quantum computational algorithms.  Components of quantum circuits are discussed individually, and then built up to show how algorithms such as the quantum Fourier transform and Grover¿s search algorithm can be implemented, and how such algorithms can be used in applications such as factorizing integers using Shor¿s algorithm.  A suitable computational frameworkis introduced and used as a tool to implement the algorithms on simulated quantum computers.

This module teaches cloud computing concepts and the security of the cloud platform to enable students to understand how to secure workloads on cloud. The topics including basic concepts and the core security services needed to secure workloads and the processing of sensitive data. To re-enforce learning, the module will require students to work in teams to configure the cloud platform using infrastructure automation.

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.

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.

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. The resulting framework, known as the Internet of Things (IoT), incorporates several technologies, including wireless sensor networks, pervasive systems, ambient intelligence, context awareness, and distributed systems. The IoT module is designed to provide a comprehensive understanding of how machine communications contribute to creating smart, artificial intelligence-driven environments focusing on software networking and communication systems. The module provides an overview of the key concepts and enabling technologies for the Internet of Things. It encompasses a cross-layer approach, allowing students to explore the practical aspects of sensors, actuators, and mainly communication systems for IoT across physical, media access, and network layers. This includes security considerations, satellite IoT, positioning and tracking for industrial applications, IoT Platforms (Hardware, Software), protocols and standards (e.g., 6LowPAN, ZigBee, CoAp), semantic technologies, and data and information processing mechanisms. Also, the module seamlessly integrates cutting-edge machine learning techniques tailored for IoT applications, ensuring optimal performance and adaptability.

A distributed system is built of a network of computing devices, which coordinate their actions via message passing to achieve a common goal. Typically, system components are assumed to operate concurrently, lack a global clock and to fail independently. This module shall build and consolidate your knowledge of advanced algorithms, computer networking and/or parallel computing. You shall learn to describe the principles of distributed systems, such as communication, scheduling and fault tolerance as well as to compare and contrast between a broad set of instances of distributed systems, algorithms and protocols (e.g., peer-to-peer systems, distributed hash tables, MixNets, distributed ledger technologies) within the context of various applications.    

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 examines the architectural/design needs and challenges encountered when developing and deploying secure, resilient and scalable web applications using the latest technology. It also provides an introduction to approaches used in modern Internet-scale web applications, covering technologies used in and/or developed by familiar companies such as Amazon, Netflix and Google.

This module delivers an applied perspective on international business strategy, providing students with the necessary knowledge to comprehend the principles of International Business Strategy and its practical application to Emerging Markets. Participants will gain insight into groups such as BRICS, E7, MINT, Next Eleven (N-11), and frontier markets. The curriculum offers a comprehensive framework for analysing international business strategy as an interdisciplinary endeavor within the context of global and competitive markets, thereby informing corporate decision-making. It explores the distinctions between corporate structures in Emerging Markets and those in developed economies, emphasising that successful implementation of international strategies often requires tailored approaches.Students will examine the process by which companies from Emerging Markets expand internationally, investigating both their success and failures. Furthermore, the module addresses contemporary issues relevant to international business strategy, including change management, technology strategy, sustainability initiatives, and Non-Market Strategy. The module is structured around three principal themes: Define international business strategy.Examining the international business strategy as applied to Emerging Markets.Explore how international business strategies are developed and implemented during periods of global and internal challenges.Through theoretical and practical exploration, students will understand how business strategy can be formulated in response to environmental and internal factors

In recent years, AI has seen tremendous growth due in large part to more powerful computers, larger scale data and techniques to establish comprehensive framework through deeper neural networks. This module introduces a wide range of deep learning and the latest state of art techniques in AI for serving the world through innovation, understanding and compassion. Fundamental concepts on applied maths and establishment on effective learning objectives that thread through key elements in machine learning techniques will be discussed throughout the module. Students will study how to build suitable AI systems that can operate in complicated, real-world environments. The module also prepares students to explore further challenges and opportunities to work with advanced AI and bring them to new frontiers. The module content will typically be updated each year reflecting the latest evolution in AI.  

The course is an introduction in formal methods for system specification and verification. It will focus on logic-based formalisms and techniques, and specifically on model checking. The main logics taught will be temporal logics, which are mainstream  in verification, especially analysis of hardware systems. Other logics and verification techniques (such as theorem proving) will be included to a smaller extended. Model checkers will be used in the labs, on different system-verification problems. Elements of building model checking tools will be presented and explored. Some elements of advanced verification techniques (e.g., abstraction) will be mentioned.

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

In an era where data is a key driver of innovation and growth, practical expertise in data science is essential for solving complex business challenges. This module uses a case study-based approach to teach data science concepts and techniques, providing students with real-world contexts to develop their skills. Each case study focuses on a different industry or application, such as healthcare, finance, telecommunication, social media, and marketing, allowing students to explore the diverse ways data science creates value. Through hands-on experience with tools, methods, and datasets, students will gain practical insights into solving problems, generating predictions, and making data-driven decisions.

This module introduces students to the techniques and tools to discover known vulnerabilities in systems and applications, and use appropriate techniques to carry out attacks. A practical project-based assessment allows students to demonstrate their ability to successfully exploit vulnerable systems to exfiltrate data and circumvent access controls. The module will introduce students to the legal and ethical considerations of ethical hacking, as well as the processes by which vulnerabilities are reported, classified, documented, and mitigated by security practitioners.

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.

Expected prior learning:  Module EEE3008–Fundamentals of Digital Signal Processing  or equivalent learning about signal processing. Module purpose:  The module discusses basic concepts, signal processing methods and human computer interaction applications of speech processing and recognition including auditory perception and psychoacoustics. You will be taught how to extract salient features from speech signals, how to design a model of spoken language, how to perform recognition and training, and given an insight into current research on spontaneous speech recognition, such as speaker adaptation and solutions for robustness to noise. Demonstrations, interesting illustrations and working examples will be given. Successful students can either proceed to do PhDs or get jobs in the R & D departments of industry, i.e. jobs that are at a higher level than mere software package operators. The presented techniques have many other applications beyond speech, including expert systems and financial modelling. Module EEEM030 contributes to the development of student’s knowledge in audio and speech processing and recognition, which may be useful for their taking of other modules such as: EEEM071 Advanced Topics in Computer Vision and Deep Learning EEEM004 60 Credit Standard Project EEEM005 AI and AI Programming EEEM066 Fundamentals of Machine Learning EEEM067 AR, VR and Metaverse EEEM068 Applied Machine Learning Module EEEM030 contributes to student knowledge in audio and speech processing and thus useful for students taking 60 credit project (EEEM004) related to audio and speech processing and recognition. EEEM030 is related to EEEM005, EEEM066 and EEEM068 due to the fact that machine learning/AI techniques are used for speech and speaker recognition, therefore, EEEM030 contributes to the development of student knowledge in machine learning/AI, which is beneficial for their taking of machine learning and AI related modules such as EEEM005, EEEM066 and EEEM068. One of the applications of audio and speech processing is to apply it to AR, VR and Metaverse for spatial sound production and reproduction which is a key enabling technology for AR, VR, and Metaverse, for virtual sound reproduction. Therefore, knowledge gained from EEEM030 would be useful for the taking of the module EEEM067. Module EEE030 also benefits from knowledge gained from other modules such as: EEE3008 Digital Signal Processing EEE1033 Computer and Digital Logic EEE1035 Programming  in C EEE3042 Audio and Video Processing EEE3032 Computer Vision and Pattern Recognition The modules EEE1033 and EEE1035 provide students with some useful skills in programming, which will be beneficial for them to complete the computer programming based coursework components, by turning the signal processing theories and methods into working program codes. The module EEE3008 provides students with knowledge and skills on fundamental digital signal processing skills which are essential in understanding the application of these skills to audio and speech data. EEE3042 covers both audio and video processing and coding, and the audio related materials are highly relevant and thus useful for the EEEM030 module. The pattern recognition skills gained from EEE3032 would be useful for understanding the use of pattern recognition algorithms to speech data for achieving speech and speaker recognition.  

The module introduces general cryptographic concepts, challenges and goals and then focuses on foundational cryptographic primitives and algorithms in the fields of symmetric (a.k.a. private-key) and asymmetric (a.k.a. public-key) cryptography. The module will introduce mathematical foundations that are essential for the functionality and security of cryptographic algorithms and then focus on the security definitions and constructions of concrete algorithms for authentication, confidentiality and integrity. In labs students will develop and test their understanding of the material and learn how to implement and use existing algorithms from symmetric and asymmetric cryptography.

This module provides knowledge about advanced digital circuit design and the hardware description language VHDL. The practical part of the course is concerned with FPGA implementation, using modern CAD tools and FPGA prototyping boards. This module builds on from many Electronic Engineering modules at undergraduate level in the topics of Circuit Design and Processor design such EEE2045 Electrical Science II and will lead on to EEEM059 Space Avionics

Quantum computing exploits the principles of superposition and entanglement to perform certain computational tasks more efficiently than is possible using classical computers. While quantum computing relies on quantum physics, it is possible to understand and apply quantum computation without detailed prior training in quantum mechanics. This module provides an introduction to quantum computing from a computational and algorithmic perspective. It introduces the mathematical framework used to describe qubits and quantum logic, develops the concept of quantum circuits, and explores how quantum algorithms may be applied to optimization problems of practical relevance. The module emphasises how quantum information is processed, rather than how quantum hardware is physically realised. This module is suitable for students from Physics, Computer Science, Electronic Engineering, and related disciplines.

Module purpose: This course offers an introduction to image processing and computer vision for those interested in the science and technology of machine vision. It provides background and the theory for building artificial systems that manipulate videos and images and alter or analyse their information content. This is done by various computer algorithms that are discussed, implemented and demonstrated.

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.

This module covers circuit-based quantum computers and some of the algorithms that can be implemented using them.  The idea of a classical computational algorithm and its complexity is introduced, and used as a language to discuss quantum computational algorithms.  Components of quantum circuits are discussed individually, and then built up to show how algorithms such as the quantum Fourier transform and Grover¿s search algorithm can be implemented, and how such algorithms can be used in applications such as factorizing integers using Shor¿s algorithm.  A suitable computational frameworkis introduced and used as a tool to implement the algorithms on simulated quantum computers.

This module teaches cloud computing concepts and the security of the cloud platform to enable students to understand how to secure workloads on cloud. The topics including basic concepts and the core security services needed to secure workloads and the processing of sensitive data. To re-enforce learning, the module will require students to work in teams to configure the cloud platform using infrastructure automation.

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.