The main goal of this module is to equip you with essential Python programming skills and foundational mathematical concepts crucial for AI and Data Science. Through hands-on learning, you'll develop the ability to solve real-world problems, process complex data sets, and apply key mathematical techniques like probability and matrix operations. Formative assessments will support your learning, leading to a final practical assessment that prepares you for advanced studies. Perfect for those new to computing or mathematics, this module sets the stage for your success in AI and Data Science.

The main goal of this module is to explore the essence of AI and Data Science, their origins, and their roles in solving real-world challenges. You'll delve into the duties and skills of data professionals, emphasising effective communication and ethical considerations. The module also covers the legal and societal impacts of AI, while promoting teamwork through hands-on projects that tackle AI and Data Science challenges. Supported by industry talks, you'll learn to formulate problem statements, select appropriate methods, and communicate findings effectively, preparing you for a successful career in this dynamic field.

The main goal of this module is to equip students with a solid foundation in data representation, manipulation, and processing, essential for advancing in Data Science and AI. You'll explore key topics such as Big Data, Cloud Computing, data pre-processing, and both supervised and unsupervised learning. The module also covers clustering and classification methods, including neural networks, providing hands-on experience in implementing and validating these techniques using software tools like Python. By mastering these concepts, you'll be well-prepared for more advanced modules and projects, while also enhancing your problem-solving, analytical, and independent learning skills.

A large part of the Master's involves completing the industry or research related project. This starts with the students selecting an industry or research partner, undertaking a placement in June - July, and then submitting a written dissertation of up to 20,000 words in early September.

This is primarily a self-study module designed to provide the foundation of the main dissertation, at a level considered to be of publishable quality. The project also offers students the opportunity to apply their technical skills and knowledge on current world class research problems and to develop an expert knowledge on a specific area.

The topic of the project will vary from student to student, depending on the data science specialism (eg computing may involve the design of a system, while specialism in data analytics, health or environment, are likely to be more applied, perhaps focusing upon inherent data structure and processes).

This module aims to equip students with a deep understanding of the rapidly evolving field of Artificial Intelligence (AI), focusing on both cutting-edge methods and applications. You'll explore AI's role in areas like cybersecurity, ethical considerations, human-AI interaction, and emerging technologies like Quantum AI. The module prepares you to apply AI to real-world challenges or pursue research in innovative AI techniques, ensuring alignment with current industry and academic trends. Additionally, you'll develop skills in implementing AI solutions, making ethical decisions, and effectively communicating your findings in professional settings.

The main goal of this module is to provide a comprehensive understanding of computer vision theory and its practical implementation. You'll learn key algorithms and mathematical principles behind image processing and how to apply machine learning techniques to solve complex computer vision problems. Advanced topics such as object recognition, attention mechanisms, and self-supervised learning will be explored, with applications in fields like medical imaging and autonomous vehicles. The module also enhances your proficiency in Python and state-of-the-art frameworks like PyTorch, equipping you to independently explore emerging trends and technologies in computer vision.

The main goal of this module is to provide a thorough understanding of Generative AI by combining theoretical knowledge with practical skills. You'll gain insights into foundational theories, key technologies, and practical applications of generative models. The module is designed to help you understand how Generative AI works, how to develop and deploy simple applications using cloud platforms and open-source tools, and how to navigate the ethical and societal implications of these technologies. You'll also develop skills in discussing, presenting, and integrating Generative AI into real-world applications while considering compliance and ethical guidelines.

The main goal of this module is to explore the development and optimisation of intelligent, autonomous agents capable of outperforming human capabilities in various tasks. You'll learn the core concepts of intelligent agents, from fundamental AI paradigms like rule-based systems, planning, and learning, to advanced decision-making algorithms. The module emphasises both classical and modern AI techniques, showing how traditional ideas continue to inspire powerful innovations. Through practical exercises, you'll design, implement, and validate AI algorithms, enhancing your skills in problem-solving, critical thinking, and translating complex algorithms into functional code.

The main goal of this module is to equip students with the expertise to design and implement robust technology platforms essential for effective AI and Data Science systems. You’ll explore a range of technologies like Hadoop, Spark, and PyTorch Distributed, learning how to select, configure, and optimise them for large-scale, high-performance computing. The module focuses on principles of system architecture, distributed machine learning, and scalability, with real-world case studies and industry insights. By the end, you'll be able to architect and engineer data-driven systems, critically evaluate enterprise-scale IT solutions, and implement distributed machine learning models effectively.

The main goal of this module is to provide students with cutting-edge knowledge in natural language processing (NLP) as applied in both industry and research. You'll learn how to collect, clean, and analyse language data at scale, using methods ranging from rule-based to deep learning techniques. The module covers key applications like machine translation, sentiment analysis, and summarisation, alongside discussions on language models, ethics, and bias in NLP. By the end, you'll be able to create scalable solutions for language data challenges, understand current NLP research trends, and enhance your skills in independent study, critical thinking, and effective communication.