Official description

This course will cover conceptual foundations and practical applications of a number of statistical learning methods, including Bayesean inference and machine learning. Specific topics will include the following: supervised and unsupervised learning, maximum likelihood estimation, Bayesean methods, clustering and dimensionality reduction, and neural networks. The methods will be illustrated by examples and practical exercises from various domains of science.

Additional information

Lectures will run for six weeks, from October 25 to December 6, inclusive. The deadline to drop this course from ACORN is November 17.