MSc — 2025 entry People-Centred Artificial Intelligence (Online)

The module provides an application-focused tour of machine learning for real-world healthcare research and applications 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. Self-directed coding lab activities and coursework are designed to support understanding of the theory and enable development of practical skills required for future employability.

This module introduces the students to the key ethical and regulatory issues associated with artificial intelligence, as well as to the methods of analysis of those issues used in ethics and in law. The focus of the module is on the current state of the art in the applications of artificial intelligence (in particular: of machine learning), with smaller emphasis on hypothetical future developments. The module makes use of the case study method to introduce students to ethical and regulatory (legal) questions through discussion of relevant major incidents from recent years. The module helps students develop their thinking on how to translate abstract ethical (and regulatory) requirements of fairness, explainability or privacy into engineering and business practice.

This course offers an introduction to machine learning for those interested in the science and technology of Artificial Intelligence (AI). It provides background and the theory for building fundamental artificial systems that can process a variety of data and analyse their semantic information of interest. This is implemented by various fundamental learning algorithms that will be discussed and demonstrated in an easy-to-approach manner.

Machine learning, a cornerstone of computer science and artificial intelligence, integrates methods from diverse disciplines such as statistics, applied mathematics, pattern recognition, and neural network computation. This module delves into both the theoretical underpinnings and practical applications of advanced machine learning and deep learning topics. It also explores their contributions across various sectors, including natural language processing, medical imaging, healthcare, audio analysis, and fintech. The deep learning algorithms covered in this module are extensively utilized in the industry, from innovative AI startups to leading tech giants like Google, Meta, Microsoft, Amazon, Tesla, NVIDIA etc. This curriculum provides a solid foundation in machine learning theory, equipping students with the skills to process and analyse data from different domains, such as images, videos, text, and audio. Through a combination of discussion, implementation, and demonstration, the module aims to impart a comprehensive understanding of various machine learning algorithms and their real-world applications.

This module will introduce and explore the underlying concepts and technologies of Virtual/Augmented Reality (VR/AR) and the emerging idea of the Metaverse. The module will also investigate the current and future challenges of the technologies and consider the impact it will have on industry and wider society.

This module aims to introduce students primarily with an engineering or science background with a variety of research methods, necessary to conduct research in the field of people-centred AI. The module also serves as a vehicle into equipping students with the necessary research skills to carry out a project in this cross-disciplinary area as well as important generic skills that are considered valuable to MSc graduates. The module will cover key aspects pertaining to planning and conducting research from literature reviewing to research methodology, to research ethics through to examples of innovative outputs in people-centred AI. Students will complete the module by thinking about research designs that are systematic and will inspire new research ideas. This module will be taken in the first of study, thus ensuring students gain the appropriate research skills prior to the commencement of their project in the second year. Depending on their interest, students will be able to exercise the competencies developed in this module in the context of either a technical problem concerned with developed of core AI technology or in a particular application domain concerned with the societal implications of the use of AI

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 - at 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.

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 stepping stones 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), and AI solutions to support UNSDGs (e.g. clean air, clean energy, clean water, waste management, smart manufacturing).

The purpose of this module is to provide students with a comprehensive understanding of image application. It aims to equip students with the skills to detect and classify features within an image, represent scenes in varying levels of detail, and apply machine learning and deep learning techniques for advanced image analysis tasks. The module covers a spectrum from foundational image processing methods to sophisticated algorithms for object tracking and 3D reconstruction. Students will also gain hands-on experience with MATLAB to implement concepts learned and explore the intricacies of neural network architectures for image classification processing, scene representation, and analysis through a blend of theoretical knowledge and practical.

This module explores the major legal and regulatory issues associated with the development and the 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.

Machine learning, a cornerstone of computer science and artificial intelligence, integrates methods from diverse disciplines such as statistics, applied mathematics, pattern recognition, and neural network computation. This module delves into both the theoretical underpinnings and practical applications of advanced machine learning and deep learning topics. It also explores their contributions across various sectors, including natural language processing, medical imaging, healthcare, audio analysis, and fintech. The deep learning algorithms covered in this module are extensively utilized in the industry, from innovative AI startups to leading tech giants like Google, Meta, Microsoft, Amazon, Tesla, NVIDIA etc. This curriculum provides a solid foundation in machine learning theory, equipping students with the skills to process and analyse data from different domains, such as images, videos, text, and audio. Through a combination of discussion, implementation, and demonstration, the module aims to impart a comprehensive understanding of various machine learning algorithms and their real-world applications.

The module provides an application-focused tour of machine learning for real-world healthcare research and applications 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. Self-directed coding lab activities and coursework are designed to support understanding of the theory and enable development of practical skills required for future employability.

This course offers an introduction to machine learning for those interested in the science and technology of Artificial Intelligence (AI). It provides background and the theory for building fundamental artificial systems that can process a variety of data and analyse their semantic information of interest. This is implemented by various fundamental learning algorithms that will be discussed and demonstrated in an easy-to-approach manner.

This module introduces the students to the key ethical and regulatory issues associated with artificial intelligence, as well as to the methods of analysis of those issues used in ethics and in law. The focus of the module is on the current state of the art in the applications of artificial intelligence (in particular: of machine learning), with smaller emphasis on hypothetical future developments. The module makes use of the case study method to introduce students to ethical and regulatory (legal) questions through discussion of relevant major incidents from recent years. The module helps students develop their thinking on how to translate abstract ethical (and regulatory) requirements of fairness, explainability or privacy into engineering and business practice.

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 stepping stones 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), and AI solutions to support UNSDGs (e.g. clean air, clean energy, clean water, waste management, smart manufacturing).

The purpose of this module is to provide students with a comprehensive understanding of image application. It aims to equip students with the skills to detect and classify features within an image, represent scenes in varying levels of detail, and apply machine learning and deep learning techniques for advanced image analysis tasks. The module covers a spectrum from foundational image processing methods to sophisticated algorithms for object tracking and 3D reconstruction. Students will also gain hands-on experience with MATLAB to implement concepts learned and explore the intricacies of neural network architectures for image classification processing, scene representation, and analysis through a blend of theoretical knowledge and practical.

This module explores the major legal and regulatory issues associated with the development and the 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 aims to introduce students primarily with an engineering or science background with a variety of research methods, necessary to conduct research in the field of people-centred AI. The module also serves as a vehicle into equipping students with the necessary research skills to carry out a project in this cross-disciplinary area as well as important generic skills that are considered valuable to MSc graduates. The module will cover key aspects pertaining to planning and conducting research from literature reviewing to research methodology, to research ethics through to examples of innovative outputs in people-centred AI. Students will complete the module by thinking about research designs that are systematic and will inspire new research ideas. This module will be taken in the first of study, thus ensuring students gain the appropriate research skills prior to the commencement of their project in the second year. Depending on their interest, students will be able to exercise the competencies developed in this module in the context of either a technical problem concerned with developed of core AI technology or in a particular application domain concerned with the societal implications of the use of AI

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 - at 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.

This module will introduce and explore the underlying concepts and technologies of Virtual/Augmented Reality (VR/AR) and the emerging idea of the Metaverse. The module will also investigate the current and future challenges of the technologies and consider the impact it will have on industry and wider society.

The module provides an application-focused tour of machine learning for real-world healthcare research and applications 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. Self-directed coding lab activities and coursework are designed to support understanding of the theory and enable development of practical skills required for future employability.

This module introduces the students to the key ethical and regulatory issues associated with artificial intelligence, as well as to the methods of analysis of those issues used in ethics and in law. The focus of the module is on the current state of the art in the applications of artificial intelligence (in particular: of machine learning), with smaller emphasis on hypothetical future developments. The module makes use of the case study method to introduce students to ethical and regulatory (legal) questions through discussion of relevant major incidents from recent years. The module helps students develop their thinking on how to translate abstract ethical (and regulatory) requirements of fairness, explainability or privacy into engineering and business practice.

This course offers an introduction to machine learning for those interested in the science and technology of Artificial Intelligence (AI). It provides background and the theory for building fundamental artificial systems that can process a variety of data and analyse their semantic information of interest. This is implemented by various fundamental learning algorithms that will be discussed and demonstrated in an easy-to-approach manner.

Machine learning, a cornerstone of computer science and artificial intelligence, integrates methods from diverse disciplines such as statistics, applied mathematics, pattern recognition, and neural network computation. This module delves into both the theoretical underpinnings and practical applications of advanced machine learning and deep learning topics. It also explores their contributions across various sectors, including natural language processing, medical imaging, healthcare, audio analysis, and fintech. The deep learning algorithms covered in this module are extensively utilized in the industry, from innovative AI startups to leading tech giants like Google, Meta, Microsoft, Amazon, Tesla, NVIDIA etc. This curriculum provides a solid foundation in machine learning theory, equipping students with the skills to process and analyse data from different domains, such as images, videos, text, and audio. Through a combination of discussion, implementation, and demonstration, the module aims to impart a comprehensive understanding of various machine learning algorithms and their real-world applications.

This module will introduce and explore the underlying concepts and technologies of Virtual/Augmented Reality (VR/AR) and the emerging idea of the Metaverse. The module will also investigate the current and future challenges of the technologies and consider the impact it will have on industry and wider society.

This module aims to introduce students primarily with an engineering or science background with a variety of research methods, necessary to conduct research in the field of people-centred AI. The module also serves as a vehicle into equipping students with the necessary research skills to carry out a project in this cross-disciplinary area as well as important generic skills that are considered valuable to MSc graduates. The module will cover key aspects pertaining to planning and conducting research from literature reviewing to research methodology, to research ethics through to examples of innovative outputs in people-centred AI. Students will complete the module by thinking about research designs that are systematic and will inspire new research ideas. This module will be taken in the first of study, thus ensuring students gain the appropriate research skills prior to the commencement of their project in the second year. Depending on their interest, students will be able to exercise the competencies developed in this module in the context of either a technical problem concerned with developed of core AI technology or in a particular application domain concerned with the societal implications of the use of AI

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 - at 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.

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 stepping stones 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), and AI solutions to support UNSDGs (e.g. clean air, clean energy, clean water, waste management, smart manufacturing).

The purpose of this module is to provide students with a comprehensive understanding of image application. It aims to equip students with the skills to detect and classify features within an image, represent scenes in varying levels of detail, and apply machine learning and deep learning techniques for advanced image analysis tasks. The module covers a spectrum from foundational image processing methods to sophisticated algorithms for object tracking and 3D reconstruction. Students will also gain hands-on experience with MATLAB to implement concepts learned and explore the intricacies of neural network architectures for image classification processing, scene representation, and analysis through a blend of theoretical knowledge and practical.

This module explores the major legal and regulatory issues associated with the development and the 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.

Machine learning, a cornerstone of computer science and artificial intelligence, integrates methods from diverse disciplines such as statistics, applied mathematics, pattern recognition, and neural network computation. This module delves into both the theoretical underpinnings and practical applications of advanced machine learning and deep learning topics. It also explores their contributions across various sectors, including natural language processing, medical imaging, healthcare, audio analysis, and fintech. The deep learning algorithms covered in this module are extensively utilized in the industry, from innovative AI startups to leading tech giants like Google, Meta, Microsoft, Amazon, Tesla, NVIDIA etc. This curriculum provides a solid foundation in machine learning theory, equipping students with the skills to process and analyse data from different domains, such as images, videos, text, and audio. Through a combination of discussion, implementation, and demonstration, the module aims to impart a comprehensive understanding of various machine learning algorithms and their real-world applications.

The module provides an application-focused tour of machine learning for real-world healthcare research and applications 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. Self-directed coding lab activities and coursework are designed to support understanding of the theory and enable development of practical skills required for future employability.

This course offers an introduction to machine learning for those interested in the science and technology of Artificial Intelligence (AI). It provides background and the theory for building fundamental artificial systems that can process a variety of data and analyse their semantic information of interest. This is implemented by various fundamental learning algorithms that will be discussed and demonstrated in an easy-to-approach manner.

This module introduces the students to the key ethical and regulatory issues associated with artificial intelligence, as well as to the methods of analysis of those issues used in ethics and in law. The focus of the module is on the current state of the art in the applications of artificial intelligence (in particular: of machine learning), with smaller emphasis on hypothetical future developments. The module makes use of the case study method to introduce students to ethical and regulatory (legal) questions through discussion of relevant major incidents from recent years. The module helps students develop their thinking on how to translate abstract ethical (and regulatory) requirements of fairness, explainability or privacy into engineering and business practice.

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 stepping stones 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), and AI solutions to support UNSDGs (e.g. clean air, clean energy, clean water, waste management, smart manufacturing).

The purpose of this module is to provide students with a comprehensive understanding of image application. It aims to equip students with the skills to detect and classify features within an image, represent scenes in varying levels of detail, and apply machine learning and deep learning techniques for advanced image analysis tasks. The module covers a spectrum from foundational image processing methods to sophisticated algorithms for object tracking and 3D reconstruction. Students will also gain hands-on experience with MATLAB to implement concepts learned and explore the intricacies of neural network architectures for image classification processing, scene representation, and analysis through a blend of theoretical knowledge and practical.

This module explores the major legal and regulatory issues associated with the development and the 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 aims to introduce students primarily with an engineering or science background with a variety of research methods, necessary to conduct research in the field of people-centred AI. The module also serves as a vehicle into equipping students with the necessary research skills to carry out a project in this cross-disciplinary area as well as important generic skills that are considered valuable to MSc graduates. The module will cover key aspects pertaining to planning and conducting research from literature reviewing to research methodology, to research ethics through to examples of innovative outputs in people-centred AI. Students will complete the module by thinking about research designs that are systematic and will inspire new research ideas. This module will be taken in the first of study, thus ensuring students gain the appropriate research skills prior to the commencement of their project in the second year. Depending on their interest, students will be able to exercise the competencies developed in this module in the context of either a technical problem concerned with developed of core AI technology or in a particular application domain concerned with the societal implications of the use of AI

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 - at 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.

This module will introduce and explore the underlying concepts and technologies of Virtual/Augmented Reality (VR/AR) and the emerging idea of the Metaverse. The module will also investigate the current and future challenges of the technologies and consider the impact it will have on industry and wider society.

The module provides an application-focused tour of machine learning for real-world healthcare research and applications 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. Self-directed coding lab activities and coursework are designed to support understanding of the theory and enable development of practical skills required for future employability.

This module introduces the students to the key ethical and regulatory issues associated with artificial intelligence, as well as to the methods of analysis of those issues used in ethics and in law. The focus of the module is on the current state of the art in the applications of artificial intelligence (in particular: of machine learning), with smaller emphasis on hypothetical future developments. The module makes use of the case study method to introduce students to ethical and regulatory (legal) questions through discussion of relevant major incidents from recent years. The module helps students develop their thinking on how to translate abstract ethical (and regulatory) requirements of fairness, explainability or privacy into engineering and business practice.

This course offers an introduction to machine learning for those interested in the science and technology of Artificial Intelligence (AI). It provides background and the theory for building fundamental artificial systems that can process a variety of data and analyse their semantic information of interest. This is implemented by various fundamental learning algorithms that will be discussed and demonstrated in an easy-to-approach manner.

Machine learning, a cornerstone of computer science and artificial intelligence, integrates methods from diverse disciplines such as statistics, applied mathematics, pattern recognition, and neural network computation. This module delves into both the theoretical underpinnings and practical applications of advanced machine learning and deep learning topics. It also explores their contributions across various sectors, including natural language processing, medical imaging, healthcare, audio analysis, and fintech. The deep learning algorithms covered in this module are extensively utilized in the industry, from innovative AI startups to leading tech giants like Google, Meta, Microsoft, Amazon, Tesla, NVIDIA etc. This curriculum provides a solid foundation in machine learning theory, equipping students with the skills to process and analyse data from different domains, such as images, videos, text, and audio. Through a combination of discussion, implementation, and demonstration, the module aims to impart a comprehensive understanding of various machine learning algorithms and their real-world applications.

This module will introduce and explore the underlying concepts and technologies of Virtual/Augmented Reality (VR/AR) and the emerging idea of the Metaverse. The module will also investigate the current and future challenges of the technologies and consider the impact it will have on industry and wider society.

This module aims to introduce students primarily with an engineering or science background with a variety of research methods, necessary to conduct research in the field of people-centred AI. The module also serves as a vehicle into equipping students with the necessary research skills to carry out a project in this cross-disciplinary area as well as important generic skills that are considered valuable to MSc graduates. The module will cover key aspects pertaining to planning and conducting research from literature reviewing to research methodology, to research ethics through to examples of innovative outputs in people-centred AI. Students will complete the module by thinking about research designs that are systematic and will inspire new research ideas. This module will be taken in the first of study, thus ensuring students gain the appropriate research skills prior to the commencement of their project in the second year. Depending on their interest, students will be able to exercise the competencies developed in this module in the context of either a technical problem concerned with developed of core AI technology or in a particular application domain concerned with the societal implications of the use of AI

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 - at 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.

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 stepping stones 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), and AI solutions to support UNSDGs (e.g. clean air, clean energy, clean water, waste management, smart manufacturing).

The purpose of this module is to provide students with a comprehensive understanding of image application. It aims to equip students with the skills to detect and classify features within an image, represent scenes in varying levels of detail, and apply machine learning and deep learning techniques for advanced image analysis tasks. The module covers a spectrum from foundational image processing methods to sophisticated algorithms for object tracking and 3D reconstruction. Students will also gain hands-on experience with MATLAB to implement concepts learned and explore the intricacies of neural network architectures for image classification processing, scene representation, and analysis through a blend of theoretical knowledge and practical.

This module explores the major legal and regulatory issues associated with the development and the 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.

Machine learning, a cornerstone of computer science and artificial intelligence, integrates methods from diverse disciplines such as statistics, applied mathematics, pattern recognition, and neural network computation. This module delves into both the theoretical underpinnings and practical applications of advanced machine learning and deep learning topics. It also explores their contributions across various sectors, including natural language processing, medical imaging, healthcare, audio analysis, and fintech. The deep learning algorithms covered in this module are extensively utilized in the industry, from innovative AI startups to leading tech giants like Google, Meta, Microsoft, Amazon, Tesla, NVIDIA etc. This curriculum provides a solid foundation in machine learning theory, equipping students with the skills to process and analyse data from different domains, such as images, videos, text, and audio. Through a combination of discussion, implementation, and demonstration, the module aims to impart a comprehensive understanding of various machine learning algorithms and their real-world applications.

The module provides an application-focused tour of machine learning for real-world healthcare research and applications 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. Self-directed coding lab activities and coursework are designed to support understanding of the theory and enable development of practical skills required for future employability.

This course offers an introduction to machine learning for those interested in the science and technology of Artificial Intelligence (AI). It provides background and the theory for building fundamental artificial systems that can process a variety of data and analyse their semantic information of interest. This is implemented by various fundamental learning algorithms that will be discussed and demonstrated in an easy-to-approach manner.

This module introduces the students to the key ethical and regulatory issues associated with artificial intelligence, as well as to the methods of analysis of those issues used in ethics and in law. The focus of the module is on the current state of the art in the applications of artificial intelligence (in particular: of machine learning), with smaller emphasis on hypothetical future developments. The module makes use of the case study method to introduce students to ethical and regulatory (legal) questions through discussion of relevant major incidents from recent years. The module helps students develop their thinking on how to translate abstract ethical (and regulatory) requirements of fairness, explainability or privacy into engineering and business practice.

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 stepping stones 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), and AI solutions to support UNSDGs (e.g. clean air, clean energy, clean water, waste management, smart manufacturing).

The purpose of this module is to provide students with a comprehensive understanding of image application. It aims to equip students with the skills to detect and classify features within an image, represent scenes in varying levels of detail, and apply machine learning and deep learning techniques for advanced image analysis tasks. The module covers a spectrum from foundational image processing methods to sophisticated algorithms for object tracking and 3D reconstruction. Students will also gain hands-on experience with MATLAB to implement concepts learned and explore the intricacies of neural network architectures for image classification processing, scene representation, and analysis through a blend of theoretical knowledge and practical.

This module explores the major legal and regulatory issues associated with the development and the 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 aims to introduce students primarily with an engineering or science background with a variety of research methods, necessary to conduct research in the field of people-centred AI. The module also serves as a vehicle into equipping students with the necessary research skills to carry out a project in this cross-disciplinary area as well as important generic skills that are considered valuable to MSc graduates. The module will cover key aspects pertaining to planning and conducting research from literature reviewing to research methodology, to research ethics through to examples of innovative outputs in people-centred AI. Students will complete the module by thinking about research designs that are systematic and will inspire new research ideas. This module will be taken in the first of study, thus ensuring students gain the appropriate research skills prior to the commencement of their project in the second year. Depending on their interest, students will be able to exercise the competencies developed in this module in the context of either a technical problem concerned with developed of core AI technology or in a particular application domain concerned with the societal implications of the use of AI

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 - at 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.

This module will introduce and explore the underlying concepts and technologies of Virtual/Augmented Reality (VR/AR) and the emerging idea of the Metaverse. The module will also investigate the current and future challenges of the technologies and consider the impact it will have on industry and wider society.

The module provides an application-focused tour of machine learning for real-world healthcare research and applications 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. Self-directed coding lab activities and coursework are designed to support understanding of the theory and enable development of practical skills required for future employability.

This module introduces the students to the key ethical and regulatory issues associated with artificial intelligence, as well as to the methods of analysis of those issues used in ethics and in law. The focus of the module is on the current state of the art in the applications of artificial intelligence (in particular: of machine learning), with smaller emphasis on hypothetical future developments. The module makes use of the case study method to introduce students to ethical and regulatory (legal) questions through discussion of relevant major incidents from recent years. The module helps students develop their thinking on how to translate abstract ethical (and regulatory) requirements of fairness, explainability or privacy into engineering and business practice.

This course offers an introduction to machine learning for those interested in the science and technology of Artificial Intelligence (AI). It provides background and the theory for building fundamental artificial systems that can process a variety of data and analyse their semantic information of interest. This is implemented by various fundamental learning algorithms that will be discussed and demonstrated in an easy-to-approach manner.

Machine learning, a cornerstone of computer science and artificial intelligence, integrates methods from diverse disciplines such as statistics, applied mathematics, pattern recognition, and neural network computation. This module delves into both the theoretical underpinnings and practical applications of advanced machine learning and deep learning topics. It also explores their contributions across various sectors, including natural language processing, medical imaging, healthcare, audio analysis, and fintech. The deep learning algorithms covered in this module are extensively utilized in the industry, from innovative AI startups to leading tech giants like Google, Meta, Microsoft, Amazon, Tesla, NVIDIA etc. This curriculum provides a solid foundation in machine learning theory, equipping students with the skills to process and analyse data from different domains, such as images, videos, text, and audio. Through a combination of discussion, implementation, and demonstration, the module aims to impart a comprehensive understanding of various machine learning algorithms and their real-world applications.

This module will introduce and explore the underlying concepts and technologies of Virtual/Augmented Reality (VR/AR) and the emerging idea of the Metaverse. The module will also investigate the current and future challenges of the technologies and consider the impact it will have on industry and wider society.

This module aims to introduce students primarily with an engineering or science background with a variety of research methods, necessary to conduct research in the field of people-centred AI. The module also serves as a vehicle into equipping students with the necessary research skills to carry out a project in this cross-disciplinary area as well as important generic skills that are considered valuable to MSc graduates. The module will cover key aspects pertaining to planning and conducting research from literature reviewing to research methodology, to research ethics through to examples of innovative outputs in people-centred AI. Students will complete the module by thinking about research designs that are systematic and will inspire new research ideas. This module will be taken in the first of study, thus ensuring students gain the appropriate research skills prior to the commencement of their project in the second year. Depending on their interest, students will be able to exercise the competencies developed in this module in the context of either a technical problem concerned with developed of core AI technology or in a particular application domain concerned with the societal implications of the use of AI

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 - at 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.

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 stepping stones 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), and AI solutions to support UNSDGs (e.g. clean air, clean energy, clean water, waste management, smart manufacturing).

The purpose of this module is to provide students with a comprehensive understanding of image application. It aims to equip students with the skills to detect and classify features within an image, represent scenes in varying levels of detail, and apply machine learning and deep learning techniques for advanced image analysis tasks. The module covers a spectrum from foundational image processing methods to sophisticated algorithms for object tracking and 3D reconstruction. Students will also gain hands-on experience with MATLAB to implement concepts learned and explore the intricacies of neural network architectures for image classification processing, scene representation, and analysis through a blend of theoretical knowledge and practical.

This module explores the major legal and regulatory issues associated with the development and the 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.

Machine learning, a cornerstone of computer science and artificial intelligence, integrates methods from diverse disciplines such as statistics, applied mathematics, pattern recognition, and neural network computation. This module delves into both the theoretical underpinnings and practical applications of advanced machine learning and deep learning topics. It also explores their contributions across various sectors, including natural language processing, medical imaging, healthcare, audio analysis, and fintech. The deep learning algorithms covered in this module are extensively utilized in the industry, from innovative AI startups to leading tech giants like Google, Meta, Microsoft, Amazon, Tesla, NVIDIA etc. This curriculum provides a solid foundation in machine learning theory, equipping students with the skills to process and analyse data from different domains, such as images, videos, text, and audio. Through a combination of discussion, implementation, and demonstration, the module aims to impart a comprehensive understanding of various machine learning algorithms and their real-world applications.

The module provides an application-focused tour of machine learning for real-world healthcare research and applications 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. Self-directed coding lab activities and coursework are designed to support understanding of the theory and enable development of practical skills required for future employability.

This course offers an introduction to machine learning for those interested in the science and technology of Artificial Intelligence (AI). It provides background and the theory for building fundamental artificial systems that can process a variety of data and analyse their semantic information of interest. This is implemented by various fundamental learning algorithms that will be discussed and demonstrated in an easy-to-approach manner.

This module introduces the students to the key ethical and regulatory issues associated with artificial intelligence, as well as to the methods of analysis of those issues used in ethics and in law. The focus of the module is on the current state of the art in the applications of artificial intelligence (in particular: of machine learning), with smaller emphasis on hypothetical future developments. The module makes use of the case study method to introduce students to ethical and regulatory (legal) questions through discussion of relevant major incidents from recent years. The module helps students develop their thinking on how to translate abstract ethical (and regulatory) requirements of fairness, explainability or privacy into engineering and business practice.

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 stepping stones 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), and AI solutions to support UNSDGs (e.g. clean air, clean energy, clean water, waste management, smart manufacturing).

The purpose of this module is to provide students with a comprehensive understanding of image application. It aims to equip students with the skills to detect and classify features within an image, represent scenes in varying levels of detail, and apply machine learning and deep learning techniques for advanced image analysis tasks. The module covers a spectrum from foundational image processing methods to sophisticated algorithms for object tracking and 3D reconstruction. Students will also gain hands-on experience with MATLAB to implement concepts learned and explore the intricacies of neural network architectures for image classification processing, scene representation, and analysis through a blend of theoretical knowledge and practical.

This module explores the major legal and regulatory issues associated with the development and the 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 aims to introduce students primarily with an engineering or science background with a variety of research methods, necessary to conduct research in the field of people-centred AI. The module also serves as a vehicle into equipping students with the necessary research skills to carry out a project in this cross-disciplinary area as well as important generic skills that are considered valuable to MSc graduates. The module will cover key aspects pertaining to planning and conducting research from literature reviewing to research methodology, to research ethics through to examples of innovative outputs in people-centred AI. Students will complete the module by thinking about research designs that are systematic and will inspire new research ideas. This module will be taken in the first of study, thus ensuring students gain the appropriate research skills prior to the commencement of their project in the second year. Depending on their interest, students will be able to exercise the competencies developed in this module in the context of either a technical problem concerned with developed of core AI technology or in a particular application domain concerned with the societal implications of the use of AI

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 - at 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.

This module will introduce and explore the underlying concepts and technologies of Virtual/Augmented Reality (VR/AR) and the emerging idea of the Metaverse. The module will also investigate the current and future challenges of the technologies and consider the impact it will have on industry and wider society.