Main Content
M3L Courses and Seminars (Winter Semester 2025/26)
The M3L group explores how people interact with and learn from intelligent systems. Our winter semester 2025/26 courses span computer vision, AI in education, and human-computer interaction.
Computer Vision (Lecture & Exercise)
Course description will be added shortly. For up-to-date information on content, requirements, and enrollment, please consult the university course catalogue on ILIAS.
Advanced Topics in Computer Vision and Pattern Recognition (Seminar)
Visual perception is a fundamental human ability, enabling us to navigate safely through our environment and make informed decisions. Computer Vision (CV) aims to replicate this ability in machines, enabling them to understand and gain insights from digital images and videos. By identifying meaningful patterns in visual data, CV enhances automated planning and decision-making. Today, a wide range of deep-learning architectures enables complex tasks like object detection and image generation, as well as fusing visual data with other modalities such as text prompts. The Master's-level seminar, "Advanced Topics in Computer Vision and Pattern Recognition," allows students to explore and discuss the latest trends and state-of-the-art methods for solving challenging computer vision tasks.
Artificial Intelligence in Educational Contexts (Seminar)
Artificial intelligence is rapidly transforming educational contexts—from intelligent tutoring systems and automated grading to learning analytics and personalized content generation. With 500 million daily views of educational content on YouTube and nearly one in five ChatGPT conversations related to learning, AI's presence in education is undeniable. But critical questions remain: What actually works for learning? How do different stakeholders experience these tools? Who benefits, and who doesn't?
This seminar examines current AI applications in education through technical, pedagogical, and critical lenses. Students will explore diverse research areas including LLM-based tutoring, video learning analytics, knowledge tracing, bias and fairness, collaborative learning support, and AI literacy. Through presentation and discussion of recent research papers, students develop the ability to critically evaluate whether AI educational technologies adequately address interdisciplinary concerns, stakeholder needs, ethical implications, and data sensitivity.
Topics in Human-Computer Interaction: User Research for Interactive Systems (Seminar)
Everyday life is full of interactive systems—apps on our phones, AI chatbots like ChatGPT, and complex software systems to name but a few. These systems rely on core areas of Computer Science such as graphics, databases, and machine learning. Yet, technical efficiency alone doesn't tell us whether people actually find these systems useful, usable, or enjoyable.
This seminar will give you hands-on experience in studying how people use technology. You will learn practical methods to design, run, and analyze user studies that reveal how effectively interactive systems support real users. Topics include experiment design, data collection, usability testing, and interpreting results.