|Weeks||6 - 21|
|Lecture||Thursday, 10:15 - 13:00|
|Links||Course page (requires login)|
The prerequisites are
as covered e.g. in any bachelor mathematics program in the Netherlands, and as reviewed in the Appendix of the book. 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.
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.
Wouter M. Koolen
Tim van Erven