New Lectures on AI and IT Security in the Winter Semester

Five new colleagues have joined the Department of Computer Science, which is also reflected in the department's teaching program. The introduction of the Master's degree program in Information Systems has resulted in further changes.

Below, we list all new courses for the winter semester in alphabetical order and may update this list until the start of lectures.

Advanced Enterprise Computing: Management of AI (Prof. Janiesch) - INF-MSc-528
MSc (A)Inf specialization, MSc AInf AF EC/MSc WI compulsory

The module deals with advanced but fundamental socio-technical topics in Information Systems. It will initially focus on the management of artificial intelligence with topics such as: Fundamentals of artificial intelligence, including its technologies and applications, algorithmic management, AI agency and human-aI interaction, ethical considerations and legal frameworks, AI risk management, AI governance, MLOps. Students will gain theoretical and practical knowledge in the management of artificial intelligence and will be able to analyze and assess it in relation to operational issues and develop solution strategies for archetypal challenges. The lecture is accompanied by seminar-style exercises. More information here.

AI for Medical Applications (Prof. Kamp)
MSc (A)Inf/WI specialization

This module introduces core artificial intelligence methods for medical data analysis. It covers machine learning fundamentals, deep learning for medical imaging, natural language processing (including large language models), generative AI, time-series analysis, and emerging agent-based approaches. Students work hands-on with real medical datasets and GPU-based platforms to implement and evaluate algorithms. After completing the course, participants will understand key AI techniques, know how to handle various types of medical data, and be able to apply and further explore advanced AI methods in the medical domain. The course will be delivered in English.

Human-AI Interaction (Prof. Mayer)
BSc (A)Inf/WI undergraduate project

This undergraduate project course introduces students to the interdisciplinary field of Human-AI Interaction, where intelligent systems are designed and evaluated from a human-centered perspective. The course focuses on applying foundational concepts from Human-Computer Interaction (HCI) and Artificial Intelligence (AI) to the development of novel, interactive systems.

Hyperparameter Optimization (JProf. Feurer)
MSc (A)Inf/WI seminar

Hyperparameter optimization is an important step in the development of machine learning solutions and can change the performance of a model from mediocre to stellar. In this seminar, we will discuss the foundations of hyperparameter optimization: hyperparameter optimization algorithms. We will also discuss the properties of hyperparameter optimization problems and the impact of hyperparameter optimization in large-scale machine learning benchmarks. The following papers are samples of the seminar content:

• syftr: Pareto-Optimal Generative AI
• HPOD: Hyperparameter Optimization for Unsupervised Outlier Detection
• HEBO: Pushing The Limits of Sample-Efficient Hyper-parameter Optimization
• Scaling Laws for Hyperparameter Optimization

The goal of the seminar is to understand fundamental algorithms in hyperparameter optimization, how to compare them, know about recent trends, and be able to understand the survey papers of Bischl et al. (2023) and Feurer and Hutter (2019). Each participant will be assigned one research paper. Each student will give a presentation and write a short report about their topic. In addition, every student will read the papers presented by fellow students and participate in a discussion about the methods, and merits and flaws of the respective papers. Details are subject to change and will be given in a kick-off session in the first week of the semester. Also, the exact details will depend on the number of participants. The course will be delivered in English. Room, day and time: Joseph-von-Frauenhofer-Str. 25 Conference Room 3-303, Tuesday mornings, 10-12, Kickoff meeting: 14.10, 10:00h. Please contact Prof. Feurer in advance.

Intelligent User Interfaces (Prof. Mayer)
MSc (A)Inf/WI specialization

The module "Intelligent User Interfaces (IUI)" covers current topics at the intersection of Human-Computer Interaction and Machine Learning. The focus is on transferring and adapting methods from the fields of Machine Learning and Artificial Intelligence to practical questions of interactive system design, always with a human-centered perspective. Topics covered include, among others: Fundamentals of artificial intelligence and machine learning (including Python implementations), voice-based user interfaces (voice user interfaces), text processing and natural language processing (NLP), context- and environment-aware interaction in intelligent systems, intelligent text input systems and optimized keyboard layouts, recommender systems and their evaluation, explainability and transparency of intelligent systems (explainable AI), usable security, human-machine security, and trustworthy AI, introduction to human-robot interaction

Mobile Security (Prof. Rossow)
MSc (A)Inf/WI specialization

This lecture deals with various fundamental aspects of mobile operating systems and application security, with a focus on the popular open-source operating system Android. In general, it aims to increase students' awareness and understanding of security and privacy issues in this area. Students learn to address current security and privacy issues in smartphones from the perspective of the various players in the smartphone ecosystem: end users, app developers, system developers, and third parties. The course will be delivered in English. More information here.

Software Security (Prof. Rossow) - INF-MSc-338
MSc (A)Inf/WI specialization

This course introduces and discusses important theoretical and practical aspects of software security. One focus is on various attack and defense techniques. Specifically, important attack methods (e.g., buffer overflows, race conditions, use-after-free, heap overflows, etc.) and defense strategies (e.g., non-executable memory, address space layout randomization, memory tagging, etc.) are discussed. Other topics covered in the lecture include methods for software analysis and testing, such as fuzzing, symbolic execution, and reverse engineering. The course will be delivered in English. More information here.

Tabular Machine Learning (Prof. Eggensperger)
MSc (A)Inf/WI seminar

Abstract. Tabular data is everywhere and often at the core of data science tasks, from healthcare to e-commerce and the natural sciences. Yet it comes with unique challenges and research questions for machine learning: What makes tabular data different from text or images? Which models work best, and why is it hard to beat simple baselines? How do recent advances in large and pre-trained models reshape the field? What is the role of LLMs in the field of tabular tasks? In this seminar, we will explore the evolving landscape of ML for tabular data, with a special focus on predictive tasks and the rise of foundation models. We will read and discuss recent research papers and critically examine approaches. The course will be delivered in English. For more info, see https://keggensperger.github.io/teaching/2025-winter-seminar

Theoretical Foundations of Machine Learning (Prof. Kamp)
MSc (A)Inf/WI specialization

This module introduces the theoretical foundations of machine learning, with a focus on learning theory and its role in understanding the performance of learning algorithms. Students will learn how to formally analyze the generalization ability of models, understand the role of hypothesis spaces, and derive guarantees on learning performance. The course covers both classical learning theory and modern theoretical approaches that have emerged in response to the success of deep learning, where many classical assumptions break down. We will critically examine where traditional tools such as uniform convergence and VC-dimension fall short, and explore recent perspectives that aim to explain the generalization behavior of highly overparameterized models. The course will be delivered in English.