Artificial Intelligence and Machine Learning

Develop the ability to critique, summarise and present scientific literature. 

Focus on the principles of high-quality academic research in AI and machine learning, including theory, novel algorithmic work and empirical research.

Learn to write a clear and persuasive research and development proposal for a short AI project that is suitable for business decision-makers or a grant application. 

Put together a use case or business case analysis and a staged plan, supported by a literature review.

Understand the fundamentals of Python and learn how to write procedural, object oriented and functional programs in Python.

Explore the issues and implications around ethics and philosophical problems, fairness and bias, and justifiable explanations in AI decision-making.

Understand the basic concepts of quantitative finance and financial engineering and build awareness of the major decision, hedging, and pricing problems in finance.

Develop a deeper understanding of optimal decision-making models, algorithms and applications to engineering, finance, and machine learning.

Learn how to design and implement modern statistical machine learning methodologies, as well as inference mechanisms.

Learn how to describe the core principles of autonomous systems learning and calculate mathematical solutions to problems using reinforcement learning theory.

Gain insights into the different types of problems that exist in machine learning and the basic algorithms used to address them.

Understand the rationale behind the theoretical end of computational neuroscience and basic principles of the tools needed to simulate the brain's intelligent behaviour in this interdisciplinary module.

Gain a flavour of different aspects of the broad and ever-expanding field of graph theory and learning, including conventional graph data analysis methods and the nascent field of graph neural networks.

Explore the intersection of robotics and human-computer interaction, covering user-centric and user study design, data analysis, and relevant theoretical foundations, in this exciting new field.

Delve into the deep connections between information theory, statistics and machine learning, in this module linking computing, statistics and geometry. Includes mathematical and computational exercises, and provides crucial theoretical background for a career in data science or machine learning.

Assess the exciting field of mobile robotics, at a time when cutting-edge robots are beginning to leave the research laboratory to tackle real-world tasks.

Explore the fundamental concepts and advanced methodologies of deep learning and relate them to real-world problems.

Develop your knowledge of machine learning further by relating back to real-world problems in computer vision and medical image analysis.

Acquire the techniques and tools needed to devise and develop natural language processing (NLP) components and applications.

Explore the role of quantum computers in providing efficient solutions to problems that are currently computationally intractable

Develop intellectual and practical skills in the use of modal logics for knowledge representation and automated reasoning in artificial intelligence.

Explore how images are formed, how they are represented on computers and how they can be processed by computers to extract semantic information.

Engage with the emerging field of robot learning and discover how robots can acquire skills and control their bodies using machine learning techniques.

Learn how to apply knowledge of domain properties and agent architectures in the representation of problems in symbolic AI.

Learn to build machine learning models that work with non-Euclidean data.

You will study embeddings, optimization techniques from Riemannian geometry, and graph theory, and apply neural networks designed for data on curved spaces and manifolds.

Examine how probability can be used to make decisions by a computer and advance your understanding of inference networks and linear and non-linear methods in statistical pattern recognition.

Gain an in-depth understanding of logic-based learning, starting from its key foundation concepts and principles before moving to more recent advances.

Investigate how modern hardware and systems make machine learning work in practice. From GPUs and TPUs to efficient ML techniques and large-scale deployment, you will get hands-on experience running models across cloud and edge environments.

This module covers the deep learning methods and systems architectures driving modern generative AI.