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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.

The module provides students with a theoretical and practical understanding of how business strategy can be formulated in the light of environmental and internal conditions with a particular emphasis on the international context of strategy making. The module equips students with a framework for understanding international business strategy as an interdisciplinary activity within the context of international and competitive markets, leading to corporate decision-making. The module is built on 3 key issues – who is strategy done for, how is strategy formulated and implemented, is strategy driven by organisations or environments. 

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.

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. 

Projects provide an efficient and effective means of delivering corporate strategy for both public and private sector organisations.  Fundamental to this is robust project sponsorship and management.  This module is focused at FHEQ Level 6 students that wish to develop, plan, manage and control projects successfully in a business environment.  This requires an awareness of general project management principles, methodologies and the tools and techniques as applied within multi-disciplined projects.  Within this module students will receive a robust grounding in the theory and practice of project management delivered by lectures and by using project management software (MS Project) in a computer laboratory setting

This research-led module will introduce students to the classic and contemporary theories of decision-making. The core aims of the module are to enable students to gain a deeper understanding of the processes that influence choice and behaviour, and to better understand how judgements and decisions can shape organisational life and managerial decisions. This module will draw upon material from a wide range of disciplines that include social psychology, cognitive psychology, organisational behaviour and behavioural economics, and will cover topics such as intuitive decision making, expert judgement, and risk-taking behaviour. Students will be encouraged to examine how the characteristics of the decision-maker, the environment and social world can help or hinder judgements and decisions, and to develop an informed understanding of the ability of individuals, including themselves, to make good decisions. A key feature of this module will be the emphasis placed upon helping students to recognise and discuss the relationship between theoretical perspectives and organisational practices. 

Successful supply chain management is critical at creating competitive advantage both at an operational and increasingly at a strategic level. An effective national and international logistics infrastructure is essential to meeting customer expectations whilst minimising service costs. Development and operation of a global logistics infrastructure is a major challenge and opportunity for supply chain managers. Supply chain management is critical to managing the complexity that global supply networks bring including the issues of sustainability and integration.

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

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 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.

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 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.

The module provides students with a theoretical and practical understanding of how business strategy can be formulated in the light of environmental and internal conditions with a particular emphasis on the international context of strategy making. The module equips students with a framework for understanding international business strategy as an interdisciplinary activity within the context of international and competitive markets, leading to corporate decision-making. The module is built on 3 key issues – who is strategy done for, how is strategy formulated and implemented, is strategy driven by organisations or environments. 

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.

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. 

Projects provide an efficient and effective means of delivering corporate strategy for both public and private sector organisations.  Fundamental to this is robust project sponsorship and management.  This module is focused at FHEQ Level 6 students that wish to develop, plan, manage and control projects successfully in a business environment.  This requires an awareness of general project management principles, methodologies and the tools and techniques as applied within multi-disciplined projects.  Within this module students will receive a robust grounding in the theory and practice of project management delivered by lectures and by using project management software (MS Project) in a computer laboratory setting

This research-led module will introduce students to the classic and contemporary theories of decision-making. The core aims of the module are to enable students to gain a deeper understanding of the processes that influence choice and behaviour, and to better understand how judgements and decisions can shape organisational life and managerial decisions. This module will draw upon material from a wide range of disciplines that include social psychology, cognitive psychology, organisational behaviour and behavioural economics, and will cover topics such as intuitive decision making, expert judgement, and risk-taking behaviour. Students will be encouraged to examine how the characteristics of the decision-maker, the environment and social world can help or hinder judgements and decisions, and to develop an informed understanding of the ability of individuals, including themselves, to make good decisions. A key feature of this module will be the emphasis placed upon helping students to recognise and discuss the relationship between theoretical perspectives and organisational practices. 

Successful supply chain management is critical at creating competitive advantage both at an operational and increasingly at a strategic level. An effective national and international logistics infrastructure is essential to meeting customer expectations whilst minimising service costs. Development and operation of a global logistics infrastructure is a major challenge and opportunity for supply chain managers. Supply chain management is critical to managing the complexity that global supply networks bring including the issues of sustainability and integration.

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

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 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.

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.