Machine learning is a data analysis and computational tool that in the last two decades brought groundbreaking progress into many modern technologies. What is more, machine learning is becoming an indispensable tool enabling progress in many scientific disciplines where knowledge is deduced from data.

This course will present some recent works in this direction. In the first part of the course works of different EPFL laboratories that use machine learning to address scientific questions in physics, chemistry, material science and biology will be presented.

Professors involved include: Lenka Zdeborova, Giuseppe Carleo, Michele Ceriotti, Philippe Schwaller, Matthieu Wyart, Alexander Mathis, Paolo De los Rios. Examples of problems covered include neural-network enhanced solutions of the Schrodinger equation in a variety of contexts, machine learning for the prediction and rationalization of chemical and physical properties of materials, analysis of proteins from their sequence and structure, or automated data analysis and modeling brain-function in neuroscience. In the second part of the lecture students will read, present and discuss selected recent articles on the subject.

Prior basic notions of machine learning are required, any of the introductory courses to machine learning is suitable. This lecture is accessible to all students across disciplines interested in natural and computational sciences.