Lady Margaret Hall, University of Oxford

The course begins with the foundation of Law, personhood, and responsibility, considering natural law, legal positivism, and the justification of state authority. It examines the notions of autonomy, Justice, and moral disobedience, investigating whether it can be morally justified to break the law, and how the law can address human dignity, inclusion, and equality. The course concludes by looking at the connections between law and citizenship, evaluating whether law can be both morally grounded and democratically plural.

This course begins with an introduction to Economic History as an interdisciplinary subject, and to the methods and sources economic historians use. It then explores key events of the past 500 years, including the Transatlantic slave-trade, colonialism, and the Industrial Revolution, examining their links to the phenomenon known as the 'Great Divergence', when levels of wealth in the Western world separated from everywhere else. It then considers the more recent phenomenon of 'Convergence', and investigate why certain countries, including Japan and China, managed to catch up with their European counterparts, whilst others fell further behind. In the final part of the course students reflect on the limits of 'Convergence', and assess whether inequality has become an immovable feature of global development. The course introduces frontline research and a variety of interdisciplinary approaches, with a particular focus on quantitative methods.

This course explores the origins of the modern museum and trace the development of the Oxford collections as a paradigm for the assembly of a material analog for every kind of historic and scientific record. It considers the museum as an instrument for the promotion of national and racial ideologies and asks how we can constructively confront a past that has often embodied ideas that are deeply at odds with 21st century sensibilities. Most importantly, classes will be taught among the peerless collections of the Ashmolean. Students will handle and examine objects ranging from Palaeolithic hand axes to 19th century silver and from neo-Assyrian sculpture to Renaissance drawings by Michelangelo and Raphael, considering fundamental questions of sources, research, and what it is truly possible to know.

The course introduces international law as a discipline. It considers the sources and subjects of international law, and explores the settlement of international disputes and enforcement of international law, including in relation to human rights. It also discusses international legal institutions such as the United Nations Security Council, the International Court of Justice, the International Law Commission, and the International Criminal Court.

In this course, students who are already familiar with the key theoretical foundations of artificial intelligence and machine learning dive deeper into the exciting capabilities of this area of research and its applications. The course begins with computer vision algorithms for classification, recognition, detection, and their implementation in deep learning libraries, before exploring autoencoders and variational autoencoders, and gaining insights into the training and application of generative adversarial networks. It then proceeds to an in-depth examination of diffusion models, including score-based diffusion models, latent diffusion models, and Stable Diffusion. The final part of the course explores even more advanced topics, including the representation of 3D objects, vision transformers, video classification, and text to image generation.

This course introduces the five core “pillars” of public health practice: biostatistics, epidemiology, environmental health, health policy and management, and social and behavioral sciences. While many public health professionals specialize in one of these areas, a well-rounded understanding across these pillars is essential to public health practice. Lectures and seminars cover key concepts and real-world examples with seminars focusing on the critical evaluation of real studies within each core area. The second part of the course focuses on applying these skills to case studies covering key public health issues from around the world.

This course introduces unsupervised learning and clustering algorithms, before exploring generative adversarial networks and deep generative models. It examines self-supervised learning, anomaly detection, flow-based models, and unsupervised representation learning. The final part of the course focuses on clustering in high-dimensional spaces, semi-supervised learning, energy-based models, and unsupervised learning for reinforcement. This intensive course offers theoretical understanding and practical experience with a focus throughout on real-world applications of deep unsupervised learning across various domains, offering career skills as well as excellent foundations for future research.