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.

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.

The module has a capacity limit and requires admission requests via ISIS before the respective deadline. From the summer term 2024, the course is a standalone module and not an elective anymore. Students who previously passed the PyML elective cannot participate in the PyML module.

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

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.

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. This course is a standalone module and not an elective anymore.

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 but NOT robotics! This course is a standalone module and NOT an elective anymore.

This module is designed with the purpose of equipping students with a comprehensive grasp of the practical application of Machine Learning techniques in both academic and industrial scenarios. Unlike other modules that predominantly delve into methodologies, this module offers a holistic perspective on the complete lifecycle of a Machine Learning project.

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.

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.

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.

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 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.

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

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 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 summarize one of the eligible papers. The summary report (1 page) will be graded on a pass/fail basis and serves as a prerequisite for giving the final paper presentation. The summary report is due at the semester’s midterm (the exact date will be announced). The final presentation, lasting 15 minutes, will be held in English at the end of the semester (the exact date will be announced). Solely the final presentation will be considered for the student’s final grade. 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. Note that from the summer term 2024, the course is a standalone module and not an elective anymore.

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

The lecture series contains of 9 lectures and two seminars at the end in which students give short presentations. 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.

Understanding the behaviour of statistical system near phase transition is crucial in Physics. In this seminar we will discuss generative models for sampling in statistical system and investigate phase transitions. Students will read and present a paper on the related topics.

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 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 seminar is designed for students who are interested in exploring reinforcement learning in greater depth across its various domains, such as theory and applications of single and multi-agent system.