Applied Deep Learning with PyTorch

5 credits

The courses is for professionals and part of the programme MAISTR ( where participants can study the entire programme or individual courses. The course is part of the course track machine learning and is held online in English.

The main content of the course concerns techniques for analysis, design, and programming of deep learning algorithms.
The course is broken down into two modules of 2.5 credits: theory and practice. The theoretical content covers basic principles of multi-layer perceptrons, spatio-temporal feature extraction with convolutional neural networks (CNNs) and recurrent neural networks (RNNs), classification and regression of big data, and producing novel data samples using generative models. The practical sessions cover the basics of programming with PyTorch, image classification, and semantic segmentation using CNNs, future image frame prediction with RNNs and image generation with generative adversarial networks.

Autumn 2024 (Distance (Internet), Varied, 33%)


Advanced level

Application code:


Entry requirements:

Degree of Bachelor of Science with a major in Computer Science and Engineering or Degree of Bachelor of Science in Engineering, Computer Science and Engineering. The degree must be equivalent to a Swedish kandidatexamen or Swedish högskoleingenjörsexamen and must have been awarded from an internationally recognised university. Including 7.5 credits programming and 7.5 credits mathematics. English 6. Exemption of the requirement in Swedish is granted.

Selection rules:

Credits: 100%

Start week:

week: 36

Number of gatherings:


Instructional time:

Various times

Language of instruction:

Teaching is in English.

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