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The aim of the module is to expose the students to modern chemical informatics, machine learning (ML) and artificial intelligence (AI) driven approaches for computational modelling and prediction, illustrated with applications to research in to the discovery of new pharmaceuticals and materials. The module will introduce the basic techniques of applying AI and ML to chemistry and the opportunity to apply these ideas to specific examples as part of a mini-project.

The aim of the module is to expose the students to modern chemical informatics, machine learning (ML) and artificial intelligence (AI) driven approaches for computational modelling and prediction, illustrated with applications to research in to the discovery of new pharmaceuticals and materials. The module will introduce the basic techniques of applying AI and ML to chemistry and the opportunity to apply these ideas to specific examples as part of a mini-project.

The aim of the module is to expose the students to modern chemical informatics, machine learning (ML) and artificial intelligence (AI) driven approaches for computational modelling and prediction, illustrated with applications to research in to the discovery of new pharmaceuticals and materials. The module will introduce the basic techniques of applying AI and ML to chemistry and the opportunity to apply these ideas to specific examples as part of a mini-project.

The first part of the course is devoted to exploring a given topic via group work, assessed via short, written summary (extended abstract) and oral presentation.
The second part consists of an individual dissertation that is assessed via a written report.
The content and the scope of both group work and individual dissertations are based on physics and astronomy ideas with the focus on independently researching them, report writing in a style of scientific papers, presentation skills as well as effective team working.

The first part of the course is devoted to exploring a given topic via group work, assessed via short, written summary (extended abstract) and oral presentation.
The second part consists of an individual dissertation that is assessed via a written report.
The content and the scope of both group work and individual dissertations are based on physics and astronomy ideas with the focus on independently researching them, report writing in a style of scientific papers, presentation skills as well as effective team working.

The first part of the course is devoted to exploring a given topic via group work, assessed via short, written summary (extended abstract) and oral presentation.
The second part consists of an individual dissertation that is assessed via a written report.
The content and the scope of both group work and individual dissertations are based on physics and astronomy ideas with the focus on independently researching them, report writing in a style of scientific papers, presentation skills as well as effective team working.

Artificial intelligence (AI) is transforming how financial institutions analyse data, manage risk, and make investment decisions. This module introduces students to the practical applications of AI and machine learning (ML) in modern banking and finance. It focuses on developing a working understanding of key methods, such as predictive modelling, natural language processing, portfolio management, and risk modelling, while emphasising interpretation, ethical use, operational considerations, and model governance. You will learn how to apply AI tools to real financial datasets to solve a range of financial decision problems, gaining experience in both the analytical design and evaluation of AI-based models. This includes an understanding of model risk, robustness, and explainability in real-world financial settings. The module aims to strike a balance between conceptual understanding and hands-on experience. To this end, we plan to employ accessible programming exercises using appropriate statistical and computational software tools commonly applied in financial analysis to illustrate how AI can extract value from complex financial data. By the end of the module, you will be able to design, evaluate, and communicate AI-based financial models with an appreciation of both your analytical power, practical limitations, and operational implications. The emphasis throughout is on practical relevance and employability – equipping you with the analytical, technical and governance-aware skills increasingly sought by asset managers, banks, investors, fintech firms, and regulators.