Current courses
Machine Learning
This course is directed to students in their Bachelor’s curriculum. After successfully completing the course, students will be able to identify examples and tasks of machine learning and assign them to the respective subcategories. They will be familiar with the main methods of supervised and unsupervised learning. They will be able to interpret deep neural network topologies, design new models, implement them, and train them. Students will learn how to independently analyze and evaluate scientific texts and complex topics, as well as develop their software in the field of machine learning to solve practical problems.
Computer Vision
This course is directed to students in their Master’s curriculum and covers digital image processing with applications in computer vision and machine learning.
The course will present issues and technologies used in modern image processing or computer vision systems. This includes image-based feature extraction and matching, segmentation, motion estimation and tracking, 3d reconstruction, classification and detection, point cloud processing. Course units are supplemented by practical application examples from industry.
Autonomous Driving
This course is directed to students in their Master’s curriculum. The students learn about the setup of modern automated vehicles. They understand the advantages and disadvantages of different sensors and know the basic vehicle system architecture. The students learn fundamental concepts, algorithms and challenges of the functional chain (perception, prediction and planning) of self-driving vehicles. Discussed concepts and algorithms are applied in practical sessions where the students need to implement parts of the software stack of a small, self-driving vehicle.