This course gives an introduction to generative models which aim at learning a probabilistic representation of the data (distribution). After completing the study of the discipline the PhD student should have knowledge about probabilistic models, knowledge about modern methods such as deep learning techniques, knowledge about ongoing developments in Machine Learning, hands-on experience with large scale machine learning problems, knowledge about how to design and develop machine learning programs using a programming language such as R or Python, and be able to think critically with real data.
Преподаватель
Кертес-Фаркаш Аттила
Course Syllabus
Abstract