Deep Learning

Education facts

Code: 5DV236

The course is about neural networks and gives an introduction to the field of deep learning. The content includes the components used to construct deep neural networks, e.g., activation functions, loss functions, regularization techniques (e.g., normalization and dropout), optimization methods (specifically variants of stochastic gradient descent), network architectures. Also covered is deep generative models. The students learn to apply their knowledge by implementing and training modern network architectures and deep learning methods on large data sets. The course is split into two modules: Theory, 5.5 credits Laboration, 2.0 credits

Entry requirements

At least 90 ECTS including at least 60 ECTS computing science, or at least 120 ECTS within a study programme. At least 7.5 ECTS programming; 7.5 ECTS data structures and algorithms; 7.5 ECTS linear algebra; 7.5 ECTS mathematical analysis (predominantly differential calculus); 7.5 ECTS mathematical statistics and probability theory; 7.5 ECTS machine learning. Proficiency in English equivalent to the level required for basic eligibility for higher studies.

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.

• Deep Learning

  Umeå University

  Start date: 25 March 2027

  End date: 6 June 2027

  Pace of study: 50 %