Teaching
Teaching in machine learning, computer vision, medical AI, and related computational methods.
Course areas
- Computer Vision
Lecture-based and practical materials on image formation, feature learning, convolutional and transformer models, segmentation, and generative methods. - Machine Learning and Deep Learning
Core methods for supervised learning, optimization, representation learning, and model evaluation. - Medical AI
Topics in medical image analysis, multimodal clinical data, reliability, and deployment-oriented evaluation. - AI for Robotics and Embodied Systems
Perception, control, planning, and multimodal reasoning for interactive systems.
Course materials
Introduction to Machine Learning · 2026
Lecture slides, an electronic textbook, tutorial notebooks, labs, and practice quizzes.
- Computer Vision 2026 LaTeX project
Lecture sources, weekly PDFs, and notebooks are maintained in the repository under theteaching/cv-2026/directory on GitHub.
Materials and format
- Slides and written lecture notes
- Practical notebooks and assignments
- Reading lists for selected topics
- Project-oriented materials for advanced courses
Student supervision
- Supervision has included topics in medical imaging, trustworthy machine learning, scientific machine learning, and computer vision.
- Specific thesis titles, student names, and defended projects can be added here as they are finalized.