Prof. Dr. Oya Beyan, Institute of Biomedical Informatics

Artificial intelligence will fundamentally change medicine, and this change has already begun – but how do the underlying methods work, and what opportunities and challenges do they present? 

Below you will find a list of introductory courses offered by the Institute of Biomedical Informatics.

• Coding Basics in Python
  January 21, 2026 – February 4, 2026, 01:00 – 03:00 pm | In-person | German & English
  Information and registration via KLIPS
   
• Intro to Data Analysis in Python
  February 9, 2026 & January 20, 2026, 10:15 am – 01:15 pm | Online | English
  Information and registration via KLIPS
   
• Introduction to Computer-Aided Medical Signal Analysis
  Febuary 5, 2026, 02:00 pm - 05:00 pm | In-person | German
  Information and registration via KLIPS
   

Prof. Dr. Aleksandar Bojchevski, Institute of Computer Science

Preliminary topics include:

• Introduction to data science: Overview of data science and its role in various industries.
• Data exploration and visualization: Use of libraries such as Matplotlib and Seaborn for data visualization.
• Data cleaning and preprocessing: Techniques for dealing with missing data, outliers, and duplicates.
• Introduction to machine learning: Basic algorithms and their applications, e.g., classification and regression.
• Ethics in data science: Data protection, data security, and ethical considerations in data collection and analysis.
• Project work: Application of the techniques and tools learned in a practical project.

By participating in this module, students will become familiar with the basics of data science and will be able to perform simple data analyses independently and interpret the results.

KLIPS Link

Prof. Dr. Aleksandar Bojchevski, Institute of Computer Science

In the Advanced Machine Learning lecture we will cover various advanced concepts, techniques, and algorithms. We will place focus both on the mathematical and theoretical aspects, as well as the practical aspects which involve implementing, training, and optimizing machine learning models using real-world datasets. The lecture is organized in four parts. In the first part we will tackle machine learning for graph data including generative models, ranking, and graph neural networks. In the second part we will cover modern generative models such as variational autoencoders, normalizing flows and generative adversarial networks. In the third part we will cover robustness including both attacks on machine learning models (evasion, poisoning) and defenses (certificates). Finally, in the last part we will cover uncertainty quantification techniques such as Bayesian neural networks, Gaussian processes and conformal prediction. Solid background in the fundamentals of machine learning is highly recommended, e.g. you should have passed our “Machine Learning“ lecture or equivalent.

KLIPS Link

Prof. Dr. Wolfgang Ketter, Faculty of Management, Economics and Social Sciences

This course is about extracting useful knowledge from data. It covers the fundamental principles or concepts that underly data science and machine learning. We are going to avoid an algorithm-centered approach whenever possible, instead of focusing on the selection and application of techniques, and the interpretation of results. We will study data science in a business context, i.e., we will mostly work with examples, case studies and data that are relevant for business.

Information and registration via KLIPS 

Prof. Dr. Axel Klawonn, Mathematical Institute

How does a pollutant spread in the soil—and how can this be reliably predicted? Based on this question, this lecture deals with the modeling of diffusion processes in porous media (e.g., soil) using partial differential equations. Partial differential equations play a central role in describing numerous physical, biological, and technical processes. In this lecture, the focus is on the numerical solution of partial differential equations that describe stationary and time-dependent diffusion processes. To this end, we use efficient spatial and temporal discretization methods. Modeling diffusion problems relevant to practice requires the determination of material parameters from measurement data. Since these are only available at discrete points, suitable data science methods must be used to predict the material parameters in areas where no data is available. For the numerical simulation of the application example of pollutant propagation in soil, we combine the finite element method, time discretization methods, and suitable data science methods. In this way, we show how numerical mathematics and data science interact in a current field of application.

The following topics will be covered in the lecture:

• Mathematical modeling of diffusion processes with partial differential equations and treatment of an application example for the spread of pollutants in soil
• Variational formulation and functional analysis fundamentals
• Numerical solution of partial differential equations with the finite element method: introduction, error estimates, implementation/programming
• Time-dependent problems and numerical methods for time discretization
• Modeling using uncertain or incomplete input data (e.g., soil measurements) with the help of suitable data science methods such as Kriging
• Combination of the finite element method with data science methods for numerical simulation of the application example

Programming is done in MATLAB; relevant prior knowledge is required.

KLIPS Link

Prof. Dr.-Ing. Tatiana von Landesberger, Institute of Computer Science

The lecture “Visualization” deals with the visual representation of data and the interactive communication of data-based content. Interactive visualization enables both the exploratory analysis of complex data sets and the effective communication of analysis results, for example in reports, presentations, or web-based applications.
The lecture teaches the basics of visualization. This includes key concepts from the areas of visualization processes, interaction, human perception, color spaces, data types, and data structures, as well as techniques for transforming, processing, and presenting data. Different data forms are covered, such as 2D, 3D, and multivariate data, time-related and spatial data, as well as networks and graphs.
In addition, fundamental methods, practical examples, and current research topics will be presented. A special focus will be placed on visualization in the context of artificial intelligence and large language models (LLMs), for example in the creation of dashboards.

KLIPS Link

Dr. Janine Weber, Mathematical Institute

Scientific Machine Learning is a new research area that is developing as a field of its own, in which techniques of scientific computing and machine learning are combined and further developed. This results in hybrid methods that are applied to the discretization of partial differential equations, the development of fast and robust solvers, and new modeling techniques.

Keywords are

1. Domain-aware scientific machine learning,
2. Mathematics-informed machine learning,
3. Interpretable scientific machine learning,
4. Machine learning-enhanced simulations,
5. Hybrid modeling (machine learning + first principle modeling).

KLIPS Link

Prof. Dr. Oya Beyan, Institute of Biomedical Informatics

Artificial intelligence will fundamentally change medicine, and this change has already begun – but how do the underlying methods work, and what opportunities and challenges do they present? 

Below you will find an overview of seminars offered by the Institute of Biomedical Informatics on the topic of artificial intelligence in medicine.

We look forward to welcoming you to one of the events.
 

• Medical AI - From Data Chaos to the Right Cancer Therapy - Data Preparation for AI in Oncology
  November 21, 2026, 10:15 am – 12:30 pm, January 28, 2026, 10:30 am – 01:30 pm | Online | German
  Information and registration via KLIPS
   
• Medical AI - Introduction to Deep Learning in Medicine and its Applications
  January 22 & 29, 2026, 02:00 pm – 05:00 pm | In-person | English
  Information and registration via KLIPS
   
• Medical AI - Explainable AI for Diabetes Prediction: A Hands-On Seminar with Python
  January 26, 2026, 10:15 am - 03:45 pm | In-person | German
  Information and registration via KLIPS
   
• Medical AI - From basics to pro: Heart Rate Variability & AI symbiosis in personalized medicine
  February 2, 2026, 09:30 am - 03:30 pm | In person | English
  Information and registration via KLIPS
   
• Medical AI - Large Language Models and Knowledge Graphs for Medical Decision Support
  February 3, 2026, 09:30 am - 03:30 pm | In-person | English
  Information and registration via KLIPS
   

Prof. Dr. Aleksandar Bojchevski, Institute of Computer Science

This seminar examines the rapidly developing field of AI safety and the challenges it poses. Topics include the development of robust and reliable AI systems, threats from adversaries, certification and verification, alignment (e.g., with human values), explainability, monitoring and prevention of accidents or misuse, agentic and multimodal security, and AI and society. The goal is to provide participants with a comprehensive insight into AI security in order to gain a thorough understanding of the field. We will examine the latest research findings through student presentations on topics of their choice and panel discussions.

KLIPS Link

Prof. Dr. Axel Klawonn, Dr. Martin Lanser, Mathematical Institute

The seminar “Mathematics of Data Science” follows the lecture “Mathematics of Data Science - an Introduction” from the 2025 summer semester. Building on the lecture, theoretical and algorithmic aspects of current topics in data science and machine learning will be explored in greater depth and detail. The seminar topics include the following subject areas:

• Dimension reduction techniques
• Classical regression
• Clustering algorithms
• Classification using support vector machines and linear discriminant analysis
• Classification using classification trees and random forests
• Classical neural networks and deep learning
• Introduction to reinforcement learning
• Reduced order models (ROMs)

KLIPS Link

Prof. Dr. Nils Reiter, Department of Digital Humanities

The advanced seminar "Artificial intelligence and (digital) society" deals with the technical aspects of current social and political debates. The aim is, on the one hand, to gain an overview of the influence of technology on political and social decisions and, on the other hand, to understand the technical fundamentals that need to be known in each area.

Specifically, we want to address the following topics (list still incomplete):

• Voting computers
• Chat control
• Electronic patient records
• Digital violence against women
• Artificial intelligence
• AI vs. copyright

KLIPS Link

Prof. Dr. Oya Beyan, Institute of Biomedical Informatics

Artificial intelligence will fundamentally change medicine, and this change has already begun – but how do the underlying methods work, and what opportunities and challenges do they present? 

Below you will find an overview of the events offered by the Institute of Biomedical Informatics on the topic of artificial intelligence in medicine!

We look forward to welcoming you to one of the events.
 

• Studium Integrale: Hands-On Data Science
  September 23, 2026 – January 27, 2026, 03:30 – 05:00 pm | Online | English
  Information and registration via KLIPS
   
• Studium Integrale: Interdisciplinary Collaboration for Digital Solutions
  October 28, 2025 – December 16, 2025 | 02:00 pm - 03:30 pm | Online | English
  Information and registration via KLIPS
   
• Studium Integrale: AI Ethics
  November 17, 2025 – December, 11, 2025, 03:00 – 04:30 | Online | English
  Information and registration via KLIPS
   
• Studium integrale: Evolution of Data Analysis
  November 18, 2025 & December 16, 2025, 03:00 pm – 05:00 pm | Online | English
  Information and registration via KLIPS