Tentative: linear methods for classification and regression, boosting, random forests, neural networks, SVMs, prototype methods, nearest neighbors, Gaussian processes, metric learning, structured learning, image classification, object detection, action recognition, pose estimation, face analysis, tracking.
The slides will be provided via sciebo.
MA-INF2201 is recommended but not required.
The course will be offered as video lecture using zoom. In order to get access, please add your email to the registration form.
Wednesday, 10:15-11:45
Friday, 10:15-11:45 (every second week)
Start: Friday, 23.04.
Theory and programming. At least 50% of the exercise points are required to qualify for exam. The programming exercises are implemented in Python and OpenCV.
Schedule
Friday, 10:15-11:45 (every second week; sometimes on Wednesday)