About

Machine learning studies automatic methods for identifying patterns in complex data and for making predictions based on past observations. In this course, we develop rigorous mathematical foundations of machine learning, in order to provide guarantees about the behaviour of learning algorithms and also to understand the inherent difficulty of learning problems.

The course will begin by providing a statistical and computational toolkit, such as concentration inequalities, fundamental algorithms and methods to analyse learning algorithms. We will cover questions such as when can we generalise well from limited amounts of data, how can we develop algorithms that are compuationally efficient, and understand statistical and computational trade-offs in learning algorithms. We will also discuss new models designed to address relevant practical questions of the day, such as learning with limited memory, communication, privacy, and labelled and unlabelled data. In addition to core concepts from machine learning, we will make connections to principal ideas from information theory, game theory and optimisation.

This is a mathematical course with several definitions, theorems statements and proofs. It is expected that students will have some familiarity with probability, algorithms, and basics of computational complexity. A formal course on Machine Learning is not required, however, students are more likely to appreciate the contents of the course if they have some familiarity with machine learning.

Pre-requisites

There are no hard pre-requisites for this course. However, it would be helpful if you have taken a majority of the following courses at Oxford (or their equivalents elsewhere). You may need to do some extra reading to fill in gaps.

• Models of Computation
• Algorithms
• Computational Complexity