United Kingdom - England

This course introduces students to a structured approach to the clinical assessment of a patient, and develops skills important to becoming a doctor, and other allied health professionals involved in patient care. It guides students through a methodical approach to history taking, examinations and choosing and interpreting common investigations, as well as developing leadership and problem-solving skills. The ethical and legal frameworks governing medical practice is explored and allows students to understand its importance in their daily work. The course also focuses on understanding the principles of professionalism, patient safety, and effective communication - applying this to patients, relatives, and colleagues. The course features interactive teaching, practical skills group problem-solving, and simulated role play sessions.

This course explores modern numerical algorithms through three connected tasks: large scale linear algebra, optimization for data science, and deep learning.  The first six lectures discuss how to approximately solve massive scale linear algebra tasks using techniques not covered in linear algebra courses. The second six lectures discuss optimization algorithms with a focus on large data science tasks. Numerical optimization is one of the most useful skills as so many tasks from science to business can be cast as optimization problems. The six seminars focus on deep learning, the key algorithmic advance driving the recent advances in machine learning and artificial intelligence. The lectures on numerical linear algebra and optimization ground this course in well understood numerical algorithms which students can study in detail, while the deep learning seminars give students the opportunity to explore the excitement driving the AI revolution. 

This course offers a comprehensive exploration into the field of Artificial Intelligence (AI), specifically designed for students with diverse backgrounds. Spanning a period of three weeks, participants are introduced to fundamental AI concepts and techniques, ranging from basic machine learning principles to advanced neural networks and ethical considerations. Through a mix of interactive lectures, hands-on coding exercises, and practical case studies, students not only acquire a theoretical understanding of AI but also develop practical skills in data pre-processing, model implementation, and ethical decision-making. The course serves as a platform for students to delve into AI's potential and ethical dimensions, cultivating insights into its applications across industries and nurturing a curiosity for further AI study.

This course introduces students to the theory, methods, and applications of linear models. The theory of the general linear model is introduced, with an emphasis on widely used methods such as regression analysis, analysis of variance, etc. Applications in various fields are used to give students experience of applying the methods using a specialized statistical software package to analyze linear models.

Health and disease are shaped by social, cultural, political, and technological forces and inextricably linked with questions of science, technology, modernity, religion, colonialism, capitalism, racism, globalization, humanitarianism, and the state. This course focuses on recent developments towards the pharmaceuticalization of health, the molecularization of life, the commodification of the body, the privatization of medical care, and the securitization of public health. These developments have fundamentally transformed today's landscape of therapeutic governance in fundamental ways.

This course offers students a grounding in the language of modern machine learning, with a focus on particular topics in linear algebra, differential calculus, probability, and statistics. Rather than focusing on theorems and their proofs, the course covers the key tools (and theorems) within the topic areas, and to illustrate these with exemplars drawn from machine learning. The course is delivered through a mixture of lectures and classes, and involves a mix of traditional lecture delivery, interactive notebooks, and problem sets.

This course examines the principal debates, features, and manifestations of Middle East politics in the 20th and 21st centuries. The course also contextualizes the Middle East as a region of the world that continually impacts on the wider international order. This course situates the Middle East, not as a single unitary manifestation of politics, but as a wider diverse and dynamic region. Political dimensions of the Middle East such as the legacy of colonialism, the democracy deficit, political economy, and contemporary conflict, as well as the role of civil society, feature as topics in the course.

The course explores how frameworks, theories, and models from a number of different areas, including cognitive neuropsychology and psycholinguistics, inform clinical assessment and remediation of aphasia. Findings from basic science, neurophysiology, imaging, and speech and language therapy are linked to increase our knowledge of the effects of the rehabilitative interventions at the level of the brain as well as their functional impact. Both emerging and established rehabilitative approaches are highlighted.

This is a highly interdisciplinary course about natural hazards and risk. This course is structured around a series of lectures and discussions aimed at understanding current methods for assessing, communicating, and visualizing risk and reducing disaster for hazards that are natural (e.g. earthquakes, volcanoes, tsunamis, mass wasting, floods, climate and extreme temperatures, multi-hazards) and environmental (e.g. heavy-metal contamination, chemical hazards), and the complex relationship that exists between these hazards and society. It is expected that students are already familiar with the material in the 2nd year Natural Hazards module (5SSG2042).

This course helps students develop rigorous quantitative skills to measure market risks in modern financial institutions. It builds on student’s introductory understanding of probability and statistics and focuses on risk management applications. This course illustrates methodologies using real financial data and a number of computer-based workshops.