Prof. Marco Loog

Delft University of Technology

Standard supervised machine learning and pattern recognition methods often do not fit the real-world requirements exactly and, hence, cannot be applied directly to solve the decision, prediction, or classification task at hand. A large class of approaches, which we collectively referred to here as partially supervised, meet some of the demands encountered in practice. The course will cover topics within the field of classification and focuses on multiple instance learning, semi-supervised learning, and, potentially, transfer learning, domain adaptation, and active learning.
The lectures will not only focus on the main lines of research within these particular areas. They also aim to provide some insight, some theoretical background, and a critical assessment of the main concepts and ideas underlying the methods. Possible directions for further research may also be sketched.
The course is completed with a one-day computer exercise session in which the participants can get some hands-on experience with concepts and methods from the lectures.

For any other information, please send an email to manuele.bicego@univr.it or umberto.castellani@univr.it;