¾ÅÉ«ÊÓƵ

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.

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.

This module examines artificial intelligence through contemporary approaches to the management of projects and project-based organisations, drawing on multidisciplinary and multi-perspective viewpoints to understand how AI influences governance, decision-making, and performance across varied organisational and delivery contexts. It introduces a wider range of concepts that support critical engagement with AI-enabled change, enabling students to compare alternative approaches, recognise their benefits and limitations, and evaluate implications for organisations and stakeholders. The module develops students’ ability to critically evaluate approaches and formulate recommendations for professional practice. It also considers the wider roles that managers/leaders, and executives play in shaping responsible adoption, value realisation, and supports students in communicating conclusions to diverse stakeholders.

This module examines artificial intelligence through contemporary approaches to the management of projects and project-based organisations, drawing on multidisciplinary and multi-perspective viewpoints to understand how AI influences governance, decision-making, and performance across varied organisational and delivery contexts. It introduces a wider range of concepts that support critical engagement with AI-enabled change, enabling students to compare alternative approaches, recognise their benefits and limitations, and evaluate implications for organisations and stakeholders. The module develops students’ ability to critically evaluate approaches and formulate recommendations for professional practice. It also considers the wider roles that managers/leaders, and executives play in shaping responsible adoption, value realisation, and supports students in communicating conclusions to diverse stakeholders.