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.

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 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 you 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 of the module, you'll be able to architect and engineer data-driven systems, critically evaluate enterprise-scale IT solutions and implement distributed machine learning models effectively.

With this module, you’ll be exposed to 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.

The Implementation element of the dissertation will cover the execution of methods, production of results, the writing of a detailed discussion of the results, and synthesis into an overall dissertation, followed by the presentation of the results to the academic supervisor and industry or research partner.

As part of the master’s programme, you will engage in a project that results in a dissertation. On completion, you are expected to be able to make value judgments relating to technologies and applications, and to justify these to peers and academic staff. The topic of the project will vary from student to student, but will be at a level commensurate with knowledge gained throughout the module. The dissertation is divided into two distinct parts: the Foundations element and the Implementation element. The Foundations element will cover requirements capture, basic specification, and literature review, facilitating the formulation of a detailed project plan and discussions on anticipated findings.

On successful completion of this module, you’ll learn to understand cross-validation of sample splitting into calibration training and validation samples, as well as be able to move to handling regression problems for large data sets via variable reduction methods such as the Lasso and Elastic Net. You’ll also gain understanding on a variety of classification methods including logistic and multinomial logistic models, regression trees, random forests and bagging and boosting. Additionally, you’ll have opportunities to examine classification methods that will culminate in neural networks presented as generalised linear modelling extensions and learn to analyse big data using K-means, PAM and CLARA, followed by mixture models and latent class analysis.