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.

**Prerequisites**

The course will be self-contained.

- Basic probability theory. (Conditional) probability and expectations. Discrete and continuous distributions.
- Basic linear algebra. Finite dimensional vector spaces. Eigen decomposition.
- Basic calculus.
- No programming will be required.

**Rules about Homework / Exam**

The grade will be composed as follows.

- 40%: weekly homework to be handed in before the next lecture.
- 30%: mid-term exam.
- 30%: final exam.

- Docent: Rianne de Heide
- Docent: Peter Grünwald
- Docent: Wouter Koolen