## An Introduction to modern tools for collaborative science

# An introduction to modern tools for collaborative 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)