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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 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.

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.

Why and how we should we regulate Artificial Intelligence [AI]? This module will systematically analyse this question with reference to existing AI laws, drawing on contemporary theoretical discourse, analytical frameworks, and a selection of case study investigations. AI is disrupting core industries and public policies with driverless cars, AI-enabled medical devices, autonomous weapons systems, personalised entertainment, digital artists, ‘intelligent’ virtual assistants, and artificial recruiters among its many applications. AI promises unprecedented potential to advance human interests. However, it also poses many risks. Current evidence suggests that some AI can misinform and manipulate human behaviour, violate individual privacy, increase socio-economic inequalities, and enhance bias in decision-making, even when used in good faith. Some even believe AI could pose an existential threat to a sustainable future. For example, acting as a double-edge sword, new AI-based environmental applications pledge to contribute to global sustainability objectives, but AI’s energy footprint raises concerns that it could impede progress on climate change.

In response to the rapid deployment of AI technology across numerous sectors, legislators and regulators enact diverse governance models to control the development, circulation and use of AI applications. Taking a pro-active approach, the European Union’s AI Act aims to introduce the world’s first comprehensive framework for regulating AI technology. In contrast, other jurisdictions depart from holistic approaches and favour sector-based regulatory interventions. These efforts seek to strike a balance between enabling progressive innovation and preventing AI causing harm to human sustainability.

This module will examine the design and implementation of current national and supranational efforts to regulate AI applications in specific areas, comparing their normative standards, institutional arrangements, and enforcement mechanisms. It will offer concepts, analytical frameworks, and methods for evaluating regulatory objectives, policy priorities, and outcomes. Moreover, it will investigate key ethical and socio-economic risks associated with the deployment of AI applications. The module adopts an approach that bridges theoretical inquiry and an examination of contemporary problematics arising with the use of AI in practice. In the first part, the module will place AI regulation within the theoretical discourse on regulating technology and examine current regulatory paradigms. In the second part, it will analyse a selection of specific case studies of AI uses and laws from diverse sectors.

Why and how we should we regulate Artificial Intelligence [AI]? This module will systematically analyse this question with reference to existing AI laws, drawing on contemporary theoretical discourse, analytical frameworks, and a selection of case study investigations. AI is disrupting core industries and public policies with driverless cars, AI-enabled medical devices, autonomous weapons systems, personalised entertainment, digital artists, ‘intelligent’ virtual assistants, and artificial recruiters among its many applications. AI promises unprecedented potential to advance human interests. However, it also poses many risks. Current evidence suggests that some AI can misinform and manipulate human behaviour, violate individual privacy, increase socio-economic inequalities, and enhance bias in decision-making, even when used in good faith. Some even believe AI could pose an existential threat to a sustainable future. For example, acting as a double-edge sword, new AI-based environmental applications pledge to contribute to global sustainability objectives, but AI’s energy footprint raises concerns that it could impede progress on climate change.

In response to the rapid deployment of AI technology across numerous sectors, legislators and regulators enact diverse governance models to control the development, circulation and use of AI applications. Taking a pro-active approach, the European Union’s AI Act aims to introduce the world’s first comprehensive framework for regulating AI technology. In contrast, other jurisdictions depart from holistic approaches and favour sector-based regulatory interventions. These efforts seek to strike a balance between enabling progressive innovation and preventing AI causing harm to human sustainability.

This module will examine the design and implementation of current national and supranational efforts to regulate AI applications in specific areas, comparing their normative standards, institutional arrangements, and enforcement mechanisms. It will offer concepts, analytical frameworks, and methods for evaluating regulatory objectives, policy priorities, and outcomes. Moreover, it will investigate key ethical and socio-economic risks associated with the deployment of AI applications. The module adopts an approach that bridges theoretical inquiry and an examination of contemporary problematics arising with the use of AI in practice. In the first part, the module will place AI regulation within the theoretical discourse on regulating technology and examine current regulatory paradigms. In the second part, it will analyse a selection of specific case studies of AI uses and laws from diverse sectors.