Course Offerings — Winter Term 2025/2026

Our group offers modules and electives:

1. Modules:

   • Only modules count toward credits at TU Berlin.

2. Electives:
   Electives are courses or seminars offered in two formats:

   • Part of a Module:

     • Integrated into one of the following:
       • Cognitive Algorithms (CA): Must include one elective.
       • Machine Learning 1/2-X (ML 1/2-X) and Deep Learning 1/2-X (DL 1/2-X): May optionally include one elective, earning three additional CPs.
     • Passing the elective is required to take the module’s exam.
     • If graded, the elective’s grade does not affect the module’s grade.
     • A passed elective remains valid for the upcoming term.

   • Standalone:

     • Not available for students seeking credits at TU Berlin.
     • Applicable only in exceptional cases (e.g., for some exchange students).
     • An elective certificate disqualifies its use as part of a module.

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

Introduction to the Julia programming language and its Machine Learning ecosystem. Learn how to write reproducible, unit-tested Julia code for ML research in Julia. No prior knowledge of Julia is required.

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 Reinforcement Learning! Please NOTE that this seminar is a standalone module and NOT an elective anymore.

Computer programs can learn useful cognitive skills. This integrated lecture communicates an intuitive understanding of elementary concepts in machine learning and their application on real data with a special focus on methods that are simple to implement. For a more advanced treatment we recommend the “Machine Learning 1” or the “Lab Course Machine Learning” modules.

This seminar takes a closer look at a mix of hot topics in machine learning including, but not limited to: Architectures in Deep Learning, Self-Supervised Learning, Generative Models, NLP, Reinforcement Learning and Variational Inference.