Problem Solving in Machine Learning (15 September - 3 October 2025)

Claire David (AIMS South Africa) and Harrison Prosper (Florida State University)

This course introduces students to the key concepts of machine learning: linear regression, gradient descent, logistic regression, regularisation, over/underfitting. The most common algorithms in supervised learning will be presented, from boosted decision trees to deep neural networks. Students will code from scratch the algorithms as well as the methods to assess the performance of their model (ROC, train/validation learning curve, bias/variance). They will learn how to improve their program through hyperparameter optimization and advanced techniques (momentum, scheduler). In unsupervised machine learning, the approaches of dimensionality reduction and clustering will be introduced and illustrated.

Bayesian Inference (15 September - 3 October 2025)

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.

Quantum Machine Learning and Foundations of Theoretical Computer Science (6-24 October 2025)

Ryan Sweke (AIMS South Africa)

Machine Learning Operations (MLOps) (6-24 October 2025)

Oliver Angelil (Ishango.ai), Cyrille Feudjio (Ishango.ai) and Vincent Frimpong (Ishango.ai).

Applied Machine Learning at Scale (3 - 21 November 2025)

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).

Monte Carlo Methods for Machine Learning (3 - 21 November 2025)

Hugo Touchette (Stellenbosch University)

Natural Language Processing and Large Language Models (24 November - 12 December 2025)

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

Bio-acoustics (24 November - 12 December 2025)

Tom Denton (Google DeepMind) and Eunice Manyasi (Strathmore University)

Neuroscience and AI (12-30 January 2026)

Christopher Currin (inait.ai)

Fluid Dynamics (12-30 January 2026)

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.

Explainable AI (XAI) (12-30 January 2026)

Inga Strümke (Norwegian University of Science and Technology)

AI for Climate Change (2-20 February 2025)

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.

Matrix and Tensor Factorization Techniques for Machine Learning (2-20 February 2026)

Guillaume Rabusseau (Université de Montréal)

Reinforcement Learning (2-20 March 2026)

Arnu Pretorius (InstaDeep)

This course provides an introduction to Reinforcement Learning (RL). It will cover fundamental topics such as Markov decision processes, dynamic programming, Monte Carlo and value-based methods including temporal difference learning, function approximation and policy gradient methods. The presentation of topics will be roughly split equally between theory and code, using notebooks and additional material. Although the focus will be on understanding the foundations of RL, the course will conclude by giving an overview of some more advanced topics and applications.

Principles of Imaging for Radio Astronomy (3-21 March 2025)

Marta Spinelli (Université Côte d'Azur) and Landman Bester (South Africa Radio Astronomy Observatory; Rhodes University)

Our best theory for the evolution of the Universe is based on the existence of a “dark” form of matter that has only gravitational interactions and an unknown form of “dark” energy that causes its accelerated expansion. Unveiling the nature of the dark sector is the next fundamental question in Cosmology and requires mapping increasingly larger volumes of the observable Universe.

The SKA Observatory is an intergovernmental organisation for ground-based astronomy in charge of building and operating the world’s largest radio facilities. One of the two observatories will be in South Africa, in the Karoo Desert. The successful exploitation of the data from SKA and its precursors will shed new light on our understanding of the formation and evolution of our Universe. This new fundamental research needs on the one hand new and innovative data analysis techniques and, on the other hand, the construction of realistic end-to-end simulations, going from the sky emission to cosmological parameter constraints.

This course will cover the timely and innovative subject of Radio Cosmology with a particular focus on the role of neutral hydrogen in unveiling the large-scale structure of the Universe. It will also discuss how AI has improved the quality of data analysis and the construction of simulations.

Course outline

• Summary of the key concepts of Modern Cosmology: evidence in favour of the Big Bang, inflation, dark matter, and dark energy. Description of the main phase of the evolution of the Universe. Concept of distance.

• Basics of Bayesian Approach in Cosmology.

• A brief summary of how we simulate the Universe. AI methods to speed up this process.

• Radio Cosmology: what we can learn about the structure and evolution of our Universe using Radio Astronomy data. Description of the SKA Observatory project. Review of the main cosmological probes accessible in the Radio band.

• 21cm Intensity Mapping: what is it, how we can do it, and what we can learn with it.

• ML techniques to analyze the data: distinguish the signal from the foreground emission.

AI and Computer Vision in Histopathology (2-20 March 2026)

Tiago Azevado (University of Cambridge)

Responsible AI (23 March - 10 April 2026)

Umang Bhatt (University of Cambridge)

The Evolution of Deep Learning (23 March - 10 April 2026)

Razvan Pascanu (Google DeepMind)

Research Project (December 2025 soft start, for 13 April - 12 June 2026)