Machine Learning

Education facts

Code: 2ST129

The course is a broad introduction to machine learning (ML) and covers supervised, unsupervised, and reinforcement learning. The course covers core ideas in ML, such as training, validation, and test of predictive models, cross-validation, (stochastic) gradient descent, ensembles, (convolutional, feed-forward, and transformer) neural networks, probabilistic mixtures, (variational) autoencoders, and bandits. The subjects are studied both theoretically, and practically in computer assignments and through an applied ML project.

Entry requirements

120 credits including 90 credits in statistics, or 120 credits including 60 credits in statistics and 30 credits in mathematics and/or computer science. 7.5 credits programming in R, Python or Julia.

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• Machine Learning

  Uppsala University

  Start date: 4 November 2026

  End date: 17 January 2027

  Pace of study: 50 %