Machine Learning Theory

Spring 2022 (weeks 6 - 21). Lectures on Thursday 10:15-13:00.

Aim | Prerequisites | Lecturers | Mode | Schedule | Exams | Material

Aim

Machine learning is one of the fastest growing areas of science, with far-reaching applications. In this course we focus on the fundamental ideas, theoretical frameworks, and rich array of mathematical tools and techniques that power machine learning. The course covers the core paradigms and results in machine learning theory with a mix of probability and statistics, combinatorics, information theory, optimization and game theory.

During the course you will learn to

This course strongly focuses on theory. (Good applied master level courses on machine learning are widely available, for example here, here and here). We will cover statistical learning theory including PAC learning, VC dimension, Rademacher complexity and Boosting, as well as online learning including prediction with expert advice, online convex optimisation, bandits and reinforcement learning.

MasterMath Website

This course is offered as part of the MasterMath program. We use the MasterMath ELO for submitting homework, and receiving grades.

Prerequisites

The prerequisites are

as covered e.g. in any bachelor mathematics program in the Netherlands, and as reviewed in the Appendix of the book [1]. The course does require general 'mathematical maturity', in particular the ability to combine insights from all three fields when proving theorems.

We offer weekly homework sets whose solution requires constructing proofs. This course will not include any programming or data.

Lecturers

The Thursday 3h slot will consist of 2h of lectures followed by a 1h TA session discussing the homework.

Mode

The grade will be composed as follows.

The average of midterm and final exam grades has to be at least 5.0 to pass the course.

It is strongly encouraged to solve and submit your weekly homework in small teams. Exams are personal.

There will be a retake possibility for either/both exams, which are 60% of the grade.

Material

We will make use of the following sources