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.

The course focuses on the Python standard library and applications relevant to machine learning, e.g., using acceleration frameworks like NumPy and Torch for the computation of tensor operations. You should know basic programming (in Python or a similar language) before enrolling in the course.

This course will treat foundational topics in Machine Learning. The scheduled topics are: Bayesian ML, Analyses (PCA, LDA), Machine Learning Theory, Classification and Regression, Latent Variable Models.

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.

The seminar provides an introduction to academic work. Students will learn how to give a presentation about a classical topic in Machine Learning. Please note that this seminar can only be taken together with CA, DL1/2-X or ML1/2-X.

Computer programs can learn useful cognitive skills. This course will take a closer look at specific applications of machine learning algorithms. With the help of their supervisors, students will read, understand, evaluate and present selected research papers on machine learning methods in different applications settings. At the end of the semester, each student will present their topic in a 15 min talk (+ 5 min discussion) in English.

The lecture series contains of 8 lectures and two seminars at the end in which students give short presentations, there is also a small multiple choice test. We will cover fundamentals of various biosignals, timeseries pre-processing, decomposition methods, feature extraction and typical challenges in multivariate / multimodal biosignal analysis. The course is based on common methods and challenges that the Intelligent Biomedical Sensing Lab is working on towards wearable neurotechnology and brain-body imaging.

During this course, we will cover a few basic mathematical concepts that are useful and frequently used in machine learning. We will go over linear algebra, analysis, and probability theory, and also discuss some contemporary applications of mathematics in machine learning. The course will be held as a block seminar over five weeks, with one lecture and one exercise session each week, accompanied by weekly homework assignments. Students who correctly solve at least 50% of the homework in total will be eligible to participate in the final written exam.

Machine learning (ML) has the potential to revolutionize healthcare, but also faces unique challenges in this area. In this seminar, we will focus on applications of ML in computational pathology. Pathology is a branch of medicine that studies and diagnoses diseases like cancer, mostly through the analysis of human tissue. Research has shown that ML can solve remarkably complex tasks in this field, e.g., detecting diseases, predicting clinical biomarkers, and forecasting patient outcomes directly from microscopic tissue images. Candidates will read, present, and discuss some of the most recent and relevant papers on ML in computational pathology.

This is a research-oriented seminar about applications of machine learning to quantum chemistry. Students will read, understand, evaluate and present selected research papers on machine learning methods in quantum chemistry. At the end of the seminar, each student will present their topic in a 20 min talk (+ 10 min questions) in English. It is possible to attend this course without prior knowledge in chemistry or physics since many papers only require a basic comprehension of the respective research topic. There is no formal registration for the kick-off meeting. It is not possible to take the seminar as a standalone course.

This course provides a series of lectures on probabilistic modeling and inference, covering the following topics: Bayesian learning, Gaussian process and Bayesian optimization, Variational inference, Generative modeling, Bayesian deep learning, Sampling methods.

Geometric Deep Learning extends deep learning to non-Euclidean structures, which might be graphs, point clouds or others. From the structure of the data naturally arise symmetries, that can be exploited to improve model performance or enhance generalization capabilities. We will study some of those methods with a special focus on graph neural networks (GNNs) that respect rotation and translation symmetries.

This is a joint, research-oriented seminar by the Machine Learning Group and the Data Management Group. Throughout the seminar, students will have the opportunity to learn about recent advances at the intersection of Machine Learning and Data Management Systems. Interested students are required to participate in the kick-off meeting, after which they will select, read, understand, and present one of the eligible papers. Moreover, the students will be required to submit a 1-page summary of their selected papers as a midterm examination. The final presentation, lasting 15 minutes (10 minutes presentation + 5 minutes of questions), will be held in English at the end of the semester (the exact date will be announced). Only the final presentation will be considered for the student’s final grade. More details will be discussed during the kick-off meeting. Note that as of the summer term 2024, this seminar is offered as an elective or standalone module.

In this seminar, foundational and current research in the area of explainable machine learning (XAI) is disseminated. Students may indicate their preferences and subsequently get assigned a paper to present. With the help of their supervisors, students will read, understand, evaluate, and present selected research papers on methods, applications, and theory in XAI.

In this seminar, foundational and recent research in generative modelling will be disseminated. Students will present and discuss a paper in this field.