AI Research Foundations (14 September - 2 October 2026)

Ayman Saeed (AIMS South Africa), Maharavosoaniaina Mari-Mar Rakotonirainy (AIMS South Africa), Similoluwa Adetoyosi Okunowo (AIMS South Africa), Toky Iriana Rajaofera (AIMS South Africa), Yassir Aladdin (AIMS South Africa)

Bayesian Inference (14 September - 2 October 2026)

Sanjoy Mahajan (Lund University)

Bayesian probability and statistics, with applications in mathematics, science, engineering, and philosophy of science. The approach emphasises the mathematical description of, and inference from, incomplete information. Topics include: nature of probability, conditional probability, Bayes' theorem, prior and posterior probabilities, Bayes factors, discrete and continuous distributions, point and compound hypotheses, hypothesis testing, likelihood principle, Shannon information, history of the Bayesian approach, and comparison with orthodox (frequentist) statistics. Applications include: medical testing, the problem of old evidence, the reproducibility crisis, drug evaluation, sequential sampling, p values, confidence intervals, legal evidence and reasoning, the Monty Hall problem, and plausible reasoning in mathematics.

Probabilistic and Scientific Machine Learning (5-23 October 2026)

Kasturi Shah (University of Washington)

We will embark on a mathematical journey through the core concepts underlying probabilistic and scientific machine learning. The course emphasises developing mathematical intuition and deriving the key foundational equations. We begin with fundamental probability rules and then move on to the various probability distributions and the types of variables and types of systems they describe. Using this foundation, the course introduces key concepts in machine learning, such as entropy, likelihood, variance, bias. We then go through an overview of simple neural networks: what they are, how they can be used, and why they work. The final stop of our journey is two key algorithms used in scientific machine learning: Physics-Informed Neural Networks and Sparse Identification of Nonlinear Dynamics. There will be opportunity to learn how to (efficiently) read a scientific paper, which is a useful research skill, and students will be encouraged to read the original papers that proposed these techniques before lecture.

Applied Machine Learning at Scale (5 - 23 October 2026)

Ulrich Paquet (Google DeepMind, AIMS South Africa)

ML and AI drive the back-ends and front-ends of many large online companies, and are set to play a transformative role in the “internet of things”. This is a practical module that looks at how ML is applied to internet-scale systems. In this module, students will build their own recommender systems from scratch. Topics covered will include A/B testing, ranking, recommender systems, and the modelling of users and entities that they engage with online (like news stories).

Introduction to Quantum Computing (2-20 November 2026)

Ryan Sweke (AIMS South Africa) and Maude Lizaire (Mila - Université de Montréal)

This course will introduce students to the basic ideas and formalism of quantum computing. In the first week we will cover the basic formalism of both single classical and quantum systems, as well as combinations of multiple such systems. This will then allow us to define the notion of entanglement, and demonstrate some of the unique features and properties of entangled quantum systems. In the second week, we will then shift our focus to quantum algorithms, and we will cover some of basic quantum algorithms, from the Deutsch-Joza algorithm through to Grover’s algorithm and the variational quantum eigensolver. Finally, in the third week, we will take a physicist's perspective, and reframe what we learned in the first week through the eyes of a physicist. This will then allow us to give a formal description of many-body quantum systems, and introduce a variety of learning theoretic problems associated with many-body quantum systems.

Visual Perception (2 - 20 November 2026)

Fernanda De La Torre (MIT), Shreya Pandit (Scale AI) and Kanique Thomas (MIT)

Machine Learning for Ecology (2-20 November 2026)

Lorène Jeantet (AIMS South Africa) and Sam Lapp (MIT)

Advanced Language and Multimodal Models (23 November - 11 December 2026)

Shamsuddeen Hassan Muhammad (Imperial College, London) and Idris Abdulmumin (University of Pretoria)

Edge Machine Learning (23 November - 11 December 2026)

Sagar Addepalli (Stanford)

Fluid Dynamics (11-29 January 2027)

Richard Katz (University of Oxford)

Fluids are all around us, from the air we breathe to the oceans that determine our climate and from oil that powers our industries to metals that are cast into machinery. The study of fluid dynamics requires sophisticated applications of mathematics and the ability to translate physical problems into mathematical language and back again. The course begins by building a fundamental understanding of viscous fluid flows in the context of unidirectional flows. In more general, higher dimensional flows, pressure gradients are generated within a fluid to deflect the flow around obstacles rather than the fluid being compressed in front of them, and an understanding of the coupling between momentum and mass conservation through the pressure field is key to the understanding and analysis of fluid motions. We will use simple experiments to illustrate and motivate our mathematical understanding of fluid flow. Prerequisite for the course is fluency with differential equations and vector calculus. No previous knowledge of fluid dynamics will be assumed.

Neuroscience and AI (11-29 January 2027)

Christopher Currin (inait.ai) and James Ferguson (Institute of Science and Technology, Austria)

This intensive three-week programme provides a foundational understanding of NeuroAI for researchers in machine learning and mathematics. The course bridges the gap between biological neural systems and artificial intelligence, exploring how principles of brain function have inspired and can continue to drive innovation in AI. By the end of this course, students will be able to critically evaluate AI models from a neuroscientific perspective and identify promising avenues for biologically-inspired research.

Explainable AI (XAI) (11-29 January 2027)

Inga Strümke (Norwegian University of Science and Technology) and Jacob Lysnæs-Larsen (Norwegian University of Science and Technology)

This course introduces explainable artificial intelligence (XAI), and covers:

• Taxonomy and terminology of XAI, and discussions of interpretable models.

• Local and global feature importance attribution methods, model-specific XAI methods.

• Basics and implementation of: counterfactuals, local and global surrogate models including LIME, the Shapley decomposition and SHAP, PDP, ALE, LOFO, SAGE, CAV and TCAV, attention weights interpretation and Grad-SAM, CNN model interpretation, activation maximization and CAM, Grad-CAM and Integrated Gradients.

AI for Climate Change (1-19 February 2027)

Neil Hart (University of Oxford), Shruti Nath (University of Oxford)

When was the last time you or your family was affected by a weather or climate event? Did the rains fail to come? Did too much rain come in one day? Did the heat make daily life difficult? Understanding the changing risk of such events is at the core of climate science research today. Building dynamical and statistical methods to better predict these events is the core of atmospheric prediction science. Fundamental research on how the Earth System works continues to support both climate science and prediction science.

In this module, we will explore the ways in which machine learning tools have been used to make sense of our weather and climate and how the rapid advances of modern AI techniques are accelerating advances across this research area. The goal of this module is to equip you with sufficient knowledge of the Earth System for you to find matches between your growing AI skill set and weather-climate problems.

In this module we will together explore, and reproduce, applications of unsupervised learning, computer vision and causal inference to such problems. Examples will be provided of new techniques in Explainable AI (XAI) to use AI as a research partner in climate science. Consideration will also be given to the very latest breakthroughs in weather prediction using GraphCast, AIFS, and similar.

Selected Topics in AI in Science (1-19 February 2027)

Eric and Wendy Schmidt AI in Science Fellows (Imperial College, London)

Bayesian Modelling and Probabilistic Programming (1-19 February 2027)

Liza Semenova (Imperial College, London)

CUDA programming for NVIDIA GPUs (1-19 March 2027)

Mike Giles (University of Oxford)

This is a 3-week course to learn how to develop parallel applications to run on NVIDIA GPUs. The aim is that by the end of the course students will be able to write relatively simple parallel programs, and will feel confident to continue learning to use CUDA through studying the code samples provided by NVIDIA on GitHub.

Geospatial Statistics and Machine Learning for Earth Observation (1-20 March 2027)

Steffen Knoblauch (University of Heidelberg)

Geophysical Fluid Dynamics (1-19 March 2027)

Grae Worster (University of Cambridge) and Jerome Neufeld (University of Cambridge)

Fluids are all around us in the natural world, from the air we breath and that determines our weather to the oceans that affect our climate and the ’solid’ earth beneath our feet, which is in constant motion, giving rise to continental drift and volcanism. This course will give an introduction to the sorts of mathematical fluid dynamics that allows us to make predictions of such geophysical flows. As time allows: we will explore the role of buoyancy-driven convection and the effect of Earth’s rotation in determining weather patterns and extreme events such as hurricanes and tornadoes; we will study flows through porous media relating to ground-water aquifers and storage of CO2 to mitigate climate change; and we will model the change of phase from liquid to solid that determines the freezing of the polar oceans, the formation of solid rocks from molten lava and the growth of the Earth’s inner core, which drives our magnetic field.

It is strongly advised that students taking this course have first taken the course in Fluid Dynamics earlier in the term.

Research Methods (1-19 March 2026)

Vilde Gjærum (DIPS AS)

Multi-model Foundation Models and Frontier Systems (22 March - 9 April 2027)

Andreas Kipf (Technische Universität Nürnberg) and Yuki Asano (Technische Universität Nürnberg)

AI for Biomedicine (19 April - 7 May 2027)

Tiago Azevado (University of Cambridge)

This course introduces the application of artificial intelligence to biomedicine, structured around two complementary domains. The first part focuses on computer vision methods for digital pathology and related biomedical imaging modalities, exploring how deep learning models extract diagnostic insights from tissue images and spectral data. The second part turns to neuroscience, covering AI approaches for brain data such as structural and functional imaging, and graph-based representations of neural connectivity. Through these perspectives, students will learn how modern AI techniques can address key challenges across biomedical research, gaining both theoretical understanding and practical skills to apply machine learning methods to complex biological data.

Research Project (February 2027 soft start, for 1 March - 18 June 2027)