The course will utilize a combination of frontal lectures and live programming demonstrations. The course will maintain a balance of approximately 50% frontal lectures and 50% hands-on sessions. The course is designed to be highly interactive, with ample opportunities for students to ask questions and engage in discussions during both the frontal lectures and hands-on sessions. Course materials and further details here.  

An introduction to modern tools for collaborative science

Each meeting will have one or a few designated papers that participants are supposed to read in advance; the paper is then presented by the module lead or a guest, followed by discussion, journal club-style.

Each meeting will have one or a few designated papers that participants are supposed to read in advance; the paper is then presented by the module lead or a guest, followed by discussion, journal club-style.

Each meeting will have one or a few designated papers that participants are supposed to read in advance; the paper is then presented by the module lead or a guest, followed by discussion, journal club-style.

Course description: Information theory, high-dimensional statistics and Bayesian inference form the power-house of modern information processing: communications, signal processing, machine learning etc. This theoretical course will introduce state-of-the-art methods of analysis and algorithms for paradigmatic models of inference in the challenging high-dimensional regime, or “BigData” regime.

Prerequisite: Bayesian Inference I Course description: Following on from Bayesian Inference I, this course addresses advanced topics in the theory and practice of Bayesian analysis. Foundational aspects are introduced that clarify the Bayesian understanding of probability, and justify its use in scientific inference. The thorny topic of prior selection is discussed at length, with particular emphasis on common pitfalls and misunderstandings.

Course description: The goals of this course are twofold: to introduce various approaches to learning with neural networks, and to develop a scientific understanding of the power and limitations of these approaches. We discuss supervised learning and generative modelling with feed-forward networks and recurrent architectures. From the theoretical point of view, we will discuss the key questions surrounding neural networks - approximation, optimisation, generalisation, and representation learning - and review the current approaches to tackle them.