After the course the student is familiar with several settings related to learning from multiple sources, and is familiar with a selection of important approaches and methods used for learning in each setting.

Learning from multiple sources denotes the problem of jointly learning from a set of (partially) related learning problems, views, or tasks. This general concept underlies several topics of research, which differ in terms of the assumptions made about the dependency structure between learning problems. During the course, we will cover a number of different learning tasks for integrating multiple sources and go through recent advances in the field. Examples of topics covered by the course include data fusion, transfer learning, multitask learning, multiview learning, and learning under covariate shift.

Enroll by sending e-mail to the lecturer (jaakko.peltonen@uta.fi) by 12.9. at the latest. After 12.9. contact the lecturer.

Modes of study

- Lectures
- Exercises (independent work)
- Exam

Recommended preceding studies

Basic mathematics and probability courses; basic competence in a scientific programming language such as matlab or R. 

Other

Course can be an optional course in
- Advanced Studies in Statistics
- Advanced Studies in Computational Methods and Programming

Further information on including this course in advanced studies, contact your study advisor or professor.