The course will begin by providing a statistical and computational toolkit, such as generalisation guarantees, 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 computationally 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 an advanced course requiring a high level of mathematical maturity. It is expected that students will have taken prior courses on machine learning, algorithms, and complexity.

NB. There is no past paper for the Advanced Machine Learning course. Whilst you may refer to the Computational Theory exam in preparation, please note that the content and scope of the version will be different.

For More You Can Check:

Corporate Video Pricing