Learning outcomes

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.

Contents

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.

Further information on prerequisites and recommendations

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

Evaluation

Belongs to following study modules

2018–2019

Teaching

Archived Teaching Schedule. Please refer to current Teaching Shedule.

MTTTS16 Learning from Multiple Sources