Deep Learning

Utbildningsfakta

Kod: 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

Behörighet

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.

Utbildningstillfällen

Varje tillfälle har egna datum och villkor. Avslutade tillfällen behålls som historik och innebär inte att en ny ansökan är öppen.

• Deep Learning

  Umeå University

  Startdatum: 25 mars 2027

  Slutdatum: 6 juni 2027

  Studietakt: 50 %