Lecture series

Mathematics of Machine Learning

First session: Francis Bach, Tuesday 27 October 2026, 15:00–17:00

Room iC, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona

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Lecture series on Mathematics of Machine Learning

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October 27, 2026

Introduction

The goal of this series of lectures is to introduce and popularize the mathematics of certain aspects of machine learning within the local community. The lectures should provide an accessible entry point into the field while also connecting the introductory material with current research directions. The activity is intended for people in mathematics, as well as for people in computer science who are especially interested in the mathematical ideas behind machine learning.

Format

Each speaker of the lecture series gives two closely related talks. The first talk, of 45 minutes, is introductory, at master’s or PhD level, and its aim is to present the relevant concepts, mathematical background, and previous results needed to understand the topic. The second talk, also of 45 minutes, focuses on research aspects linked to the first one. There is a break between the two talks.

The lectures take place in person in Room iC at the Facultat de Matemàtiques i Informàtica, Universitat de Barcelona (Edifici Històric, Gran Via de les Corts Catalanes 585, Barcelona). Attendance is free and open to all.

Lecturers and speakers

TBP

Francis Bach

INRIA – SIERRA project-team, École Normale Supérieure, PSL Research University

First lecture: Tuesday 27 October 2026, 15:00–17:00
Venue: Room iC, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona

Francis Bach is a researcher at INRIA, where he has led the SIERRA project-team since 2011. The team is part of the Computer Science Department at the École Normale Supérieure, and is joint between CNRS, ENS and INRIA. He completed his PhD in computer science at U.C. Berkeley with Michael Jordan, spent two years in the Mathematical Morphology group at the École des Mines de Paris, and was part of the WILLOW project-team at INRIA/ENS/CNRS from 2007 to 2010. He ran the ERC project SIERRA from 2009 to 2014 and now runs the ERC project SEQUOIA. He was elected to the French Academy of Sciences in 2020.

His work is in statistical machine learning, with a particular interest in optimization, sparse methods, kernel-based learning, neural networks, graphical models, and signal processing. His book Learning Theory from First Principles was published by MIT Press in December 2024.

Website: di.ens.fr/~fbach

TBP

Nicolò Cesa-Bianchi

Università degli Studi di Milano

Second lecture: 23 November 2026, 15:00–17:00 (date to be confirmed)
Venue: Room iC, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona

Nicolò Cesa-Bianchi is Professor of Computer Science at the Università degli Studi di Milano, where he has held a full professorship in the Department of Computer Science since 2002, with a joint appointment at the Politecnico di Milano since 2022. He graduated in computer science from the University of Milan in 1988 and completed his PhD there in 1993 under Alberto Bertoni, visiting UC Santa Cruz during his doctorate to work with Manfred Warmuth and David Haussler. He co-directs the Milan ELLIS unit and is a member of the Accademia Nazionale dei Lincei.

His research is in machine learning theory and online learning, in particular sequential decision-making and multi-armed bandits. He is co-author, with Gábor Lugosi, of Prediction, Learning, and Games (Cambridge University Press, 2006), and with Sébastien Bubeck of Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems.

Website: unimi.it