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Data Science

Monographic: Bayesian inference and machine learning in cosmology

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.

Monographic: Current topics in the theory of neural networks: Dynamics and Data

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.

Monographic: Machine learning in high-throughput biology

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.

Information Theory, Spin Glasses and Inference

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.

Bayesian Inference II

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.

Neural Networks

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.

Applications of Data Science to Natural Sciences

This module will present in a series of Masterclasses applications of data science to the cutting edge questions of research in various fields of Natural Sciences.

Alessandro Treves: Can information theory help understand information processing in our brain? (2h)

Carlo Baccigalupi: Data Science in Cosmological Observations (2h)

Ethics in ML and AI

This module will provide an introduction and overview to ethical issues in ML and AI, and illustrate them with contributions from guest speakers from a variety of fields. Topics covered will include bias in supervised systems, fairness, legal aspects of AI, data collection and exploitation, privacy, gender and racial inequalities, and many others. 

Unsupervised Learning and Non-parametric Methods

Course description: The aim of this course is to introduce the essential tools of unsupervised learning and dimensional reduction. These tools are of increasing use in preprocessing large databases to obtain human-readable information. We will present the most relevant dimensionality reduction algorithms for linear data manifolds, curved manifolds, and manifolds with arbitrarily complex topologies. We will then introduce a selection of approaches for estimating the probability density and the intrinsic dimension of the data manifold.

Bayesian Inference I

Course description: Probabilistic models are an appealing way to reason about systems that exhibit intrinsic and/or observational uncertainty. An important question in such models is how observational data can be used to reduce/quantify such uncertainty, leading to improved predictions and scientific discovery. Bayesian inference provides a mathematically coherent framework to incorporate knowledge from observations into models, by providing algorithms to compute posterior distributions over unobserved model variables.

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