Course Offerings — Summer Term 2024

Our group offers modules and electives:

  1. Modules: Only modules count toward credits at TU Berlin. JuML and PyML are modules from the summer term 2024 and cannot be chosen as electives anymore.
  2. Electives Electives are courses or seminars available via two formats:
    • Part of the modules Cognitive Algorithms (CA), Machine Learning 1/2-X (ML 1/2-X), or Deep Learning 1/2-X: CA must include one elective and ML/DL 1/2-X can optionally include one elective, which earns three additional CPs. Electives cannot be part of any other module. Participating in the suitable modules’ exams requires passing the respective elective. If an elective is graded, the grade does not count toward the module’s grade. A passed elective is also valid for the winter term 2024/2025.
    • Standalone: This is not possible for students that want to earn credits at TU Berlin. It is only relevant in exceptional cases, e.g., for some exchange students. An issued certificate for an elective disqualifies the student from using this elective as part of a module.

Modules

Machine Learning 2
Language English
Organizers Prof. Dr. Klaus-Robert Müller, Dr. Jacob Kauffmann
Contact j.kauffmann(∂)tu-berlin.de
ISIS link
Credit Points 9 CP (ML2) or 12 CP (ML2-X, includes one elective worth 3 CP)

This course will treat foundational topics in Machine Learning. The scheduled topics are: Low-Dimensional Embeddings (LLE, TSNE), Component Analyses (CCA, ICA), Kernel Learning (structured input, structured outputs, anomaly detection), Hidden Markov Models, Deep Learning (structured input, structured outputs, anomaly detection), Bioinformatics, Explainable AI

Cognitive Algorithms
Language English
Organizers Dr. Johannes Niediek
Contact cognitivealgorithms(∂)ml.tu-berlin.de
ISIS 37840
Module 40525
Credit Points 6 CP (includes one elective worth 3 CP)

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 2” or the “Lab Course Machine Learning” modules.

Deep Learning 2
Language English
Organizers Dr. Robert Vandermeulen, Dr. Mihail Bogojeski, Dr. Oliver Eberle
Contact vandermeulen(∂)tu-berlin.de
ISIS link
Module DL2, DL2-X
Credit Points 6 CP (DL2) or 9 CP (DL2-X)

The scheduled topics are:

  • Representation Learning
  • Attention
  • Density Estimation
  • Generative Models
  • Graph Neural Networks
  • Equivariant Neural Networks
  • Neural Ordinary Differential Equations
  • Deep Reinforcement Learning
  • Advanced Explainable AI
Lab Course Machine Learning
Language English
Organizers Dr. Mina Jamshidi, Khaled Kahouli, Jonas Dippel
Contact mina.jamshidi.idaji(∂)tu-berlin.de , khaled.kahouli@campus.tu-berlin.de , j.dippel@tu-berlin.de
Registration closed
Module 40635
Credit Points 9 CP

During this course, students implement and apply the core machine learning algorithms and analyze their performance on appropriate toy datasets. Treated are well known algorithms for dimensionality reduction, visualization, clustering, regression, and classification (incl. model selection).

Julia programming for Machine Learning (JuML)
Language English
Organizers Adrian Hill, Dr. Andreas Ziehe, Philip Naumann
Contact hill(∂)tu-berlin.de
ISIS 37588
Course website https://adrhill.github.io/julia-ml-course/
Credit Points 6 CP

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. From the summer term 2024, the course is a standalone module and not an elective anymore.

Python Programming for Machine Learning (PyML)
Language English
Organizers Jannik Wolff, Farnoush Rezaei Jafari, Dr. Eike Middell, Dr. Christopher Anders, Panagiotis Tomer Karagiannis, Ryan Gelston
Contact pyml(∂)ml.tu-berlin.de
ISIS Link (click “Als Gast anmelden” to view general information without having an ISIS account)
Module 41143
Credit Points 6 CP

The course focuses on the Python standard library and applications relevant to machine learning, e.g., using acceleration frameworks for the computation of tensor operations and visualization frameworks like Matplotlib. It is not an introductory course to programming. From the summer term 2024, the course is a standalone module and not an elective anymore. Students who previously passed the elective PyML (and used it for a module or received a certificate) cannot participate in the PyML module.

Electives

Course: Mathematical Foundations for Machine Learning (MathML)
Language English
Organizers Thomas Schnake
Contact t.schnake(∂)tu-berlin.de
ISIS TBD
Credit Points 3
Compatible Modules Machine Learning 2, Deep Learning 2, Cognitive Algorithms

The goal of this course is to freshen and deepen the mathematical foundations from the computer science program that are necessary for the lectures Cognitive Algorithms and Machine Learning. Topics come from analysis (differentiation), linear algebra (vector spaces, dot products, orthogonal vectors, matrices as linear maps, determinants, eigenvalues and eigenvectors) and probability theory (multivariate probability distributions, calculations with expectation values and variances).

Course: Bayesian Inference
Language English
Organizers Dr. Shinichi Nakajima
Contact nakajima(∂)tu-berlin.de
ISIS 39002
Credit Points 3
Compatible Modules Machine Learning 1/2, Deep Learning 1/2

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.

Seminar: Classical Topics in Machine Learning
Language English
Organizers Dr. Andreas Ziehe
Contact andreas.ziehe(∂)tu-berlin.de
ISIS 37695
Credit Points 3 CP
Compatible Modules Machine Learning 1/2, Deep Learning 2, Cognitive Algorithms

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, DL2 or ML1/2-X.

Seminar: Cognitive Algorithms
Language English
Organizers Dr. Ali Hashemi
Contact hashemi(∂)tu-berlin.de
ISIS 38867
Credit Points 3 CP
Compatible Modules Cognitive Algorithms

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.

Seminar: Machine Learning for Quantum Chemistry
Language English
Organizers Jonas Lederer
Contact jonas.lederer(∂)tu-berlin.de
ISIS 37693
Credit Points 3 CP
Compatible Modules Machine Learning 1/2, Deep Learning 1/2, Cognitive Algorithms

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 semester, 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. In the general case, it is not possible to take the seminar as a standalone course.

Seminar: Machine Learning for Data Management Systems
Language English
Organizers Prof. Dr. Matthias Böhm, Dennis Grinwald
Contact dennis.grinwald(∂)tu-berlin.de
ISIS 37892
Credit Points 3 CP
Compatible Modules Machine Learning 1/2, Cognitive Algorithms

This is a joint research-oriented seminar of the Machine Learning Group and the Data Management Group. Throughout the seminar, students will have the opportunity to learn about recent advances in 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 (if possible) programmatically evaluate one of the eligible papers (TBA), before giving a final 10-15 min presentation in the English language at the end of the semester. More details will be discussed during the Kick-off meeting. The Zoom-link for the Kick-Off meeting is written on the ISIS course-webpage.

Seminar: Explainable Machine Learning
Language English
Organizers Lorenz Linhardt
Contact l.linhardt(∂)campus.tu-berlin.de
ISIS 37694
Credit Points 3 CP
Compatible Modules Machine Learning 1/2, Deep Learning 2

In this seminar, foundational and current research in the area of explainable machine learning (XAI) will be disseminated. Students present and discuss selected papers on XAI.

Seminar: Hot Topics in ML
Language English
Organizers Marco Morik
Contact m.morik(∂)tu-berlin.de
ISIS 37692
Credit Points 3 CP
Compatible Modules Machine Learning 1/2, Deep Learning 1/2

This seminar takes a closer look at a mix of hot topics in machine learning including, but not limited to: Deep Learning, Generative Models, Reinforcement Learning and Applications of Machine Learning.

Seminar: Machine Learning in Neuroscience
Language English
Organizers Dr. Johannes Niediek, Saeed Salehi
Contact johannes.niediek(∂)tu-berlin.de
ISIS 37785
Credit Points 3 CP
Module 41148
Compatible Modules Machine Learning 1/2, Cognitive Algorithms

Machine learning plays an important and growing role in neuroscience research. The two fields interact on different levels: ML provides tools for data analysis, ML can be used to model brain processes on different scales, and sometimes neuroscience even drives developments in ML. In the summer semester 2024, the focus of the seminar will be on two themes: models of cognition, and neural decoding.

This seminar can also be taken as a standalone module.

Seminar: Machine Learning for Biomedical Signal Analysis
Language English
Organizers Dr. Alexander von Lühmann
Contact vonluehmann(∂)tu-berlin.de
ISIS 38075
Credit Points 3 CP
Compatible Modules Machine Learning 1/2, Cognitive Algorithms

Using Machine Learning for Biomedical Signal Analysis is both exciting and challenging due to its interdisciplinary nature. With a particular focus on neurotechnology and multivariate / multimodal timeseries processing, we will cover fundamentals of various biosignals such as fNIRS, EEG and ExG, techniques for pre-processing, decomposition and sensor fusion methods, feature extraction, and discuss typical challenges.

Seminar: Geometric Deep Learning
Language English
Organizers Thorben Frank
Contact thorbenjan.frank(∂)googlemail.com
ISIS TBD
Credit Points -
Compatible Modules TBD

TBD

Seminar: Generative Models
Language English
Organizers Florian Schulz
Contact florian.cf.schulz(∂)tu-berlin.de
ISIS 38481
Credit Points 3 CP
Compatible Modules Machine Learning 1/2, Deep Learning 1/2

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

Reading Group: Quantum Chemistry
Language English
Organizers Dr. Stefan Gugler
Contact stefan.gugler(∂)tu-berlin.de
ISIS TBD
Credit Points -
Compatible Modules TBD

TBD

Courses from previous semesters

Visit this link for our course offerings before the summer term 2023. Our coures offerings from the summer term 2023 are below (please click on the respective links to view the respective full page):