Machine Learning Theory [Fall 2019]
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
- Formalize learning problems in statistical and game-theoretic settings.
- Examine the statistical complexity of learning problems using the core notions of complexity.
- Analyze the statistical efficiency of learning algorithms.
- Master the design of learning strategies using proper regularization.
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 and bandits.
This course is offered as part of the MasterMath program. To participate for credit, sign up here. We use the MasterMath ELO for submitting homework, forum discussions, and receiving grades.
The only prerequisites are
- Basic probability theory. (in particular conditional probability, expectations, discrete and continuous distributions).
- Basic linear algebra (finite dimensional vector spaces, eigen decomposition)
- Basic calculus,
all at the bachelor level. The course does require general 'mathematical maturity' though, 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.