Course Offerings — Winter Term 2026/2027

Each MSc main module (ML 1, ML 2, DL 1, DL 2) comes in a standard variant (9 CP) and an -X variant (+3 CP) that adds one elective. CA (BSc) includes one elective. Electives only count as part of an -X module or CA, not standalone. Note: PyML is now called MLE.

Which electives fit which module? (WiSe 2026 offering; electives count only as the +3 CP part of an -X module or of CA)

TODO

Recommended paths

• BSc: start with CA
• MSc:
  • No CS background: take introductory CS elsewhere
  • Little math background: take introductory math first elsewhere, plan MathML as your elective
  • Little engineering background: take MLE
  • Solid math/engineering background: take ML/DL (advanced ML) or MLE (advanced engineering)
  • Projects: complete ML or DL and MLE first (or equivalent)

Deep Learning 1 is a course covering the foundations of deep learning. This includes the basics of neural networks and introductions to established architectures such as convolutional and recurrent neural networks. ML 1 and 2 are both recommended prerequisites for this course. Lectures will cover the following topics:

• Backpropagation
• Optimization
• Regularization
• Loss Functions
• Convolutional Networks
• Recurrent Neural Networks
• Autoencoders
• Structured Output
• Explainable AI

This module was renamed from “Python for Machine Learning” to “Machine Learning Engineering” in the winter term 2026/2027. Students who passed PyML cannot take MLE, because both are the same module.

Seminar on Machine learning for Neuroscience. For successful participation in the seminar, basic background in neuroscience and motivation to learn about neuroscientific topics are highly recommended. This semester the focus will be on Continual Learning! Please NOTE that this seminar is a standalone module and NOT an elective.