Advanced Machine Learning: Partially Supervised Learning
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;