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

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.

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

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).

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

This is a research-oriented seminar about applications of machine learning to graph like data.

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

TBD