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.

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.

• polytomous and ordinal regression
• generalized additive models
• nonlinear and nonparametric regression
• multilevel models
• modelling continuous and discrete longitudinal data
• missing data

Enroll by sending e-mail to mtt-studies@sis.uta.fi by 6.1.2015 at the latest. After this, participate to the first lecture or contact the lecturer.

Lectures and exercises on period III. Meetings on pediod IV are agreed during the course (meetings/seminars for coursework presentations).

If all the participants of the course are finnish, course is lectured in finnish.

Properties of high-dim data; Feature Selection; Linear feature extraction methods such as principal component analysis and linear discriminant analysis; Graphical excellence; Human perception; Nonlinear dimensionality reduction methods such as the self-organizing map and Laplacian embedding; Neighbor embedding methods such as stochastic neighbor embedding and the neighbor retrieval visualizer; Graph visualization; Graph layout methods such as LinLog.

Enroll by sending e-mail to the lecturer (jaakko.peltonen@uta.fi) by 6.1.2015 at the latest. After this, participate to the first lecture.

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
- M.Sc. programme in Algorithmics

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

• polytomous and ordinal regression
• generalized additive models
• nonlinear and nonparametric regression
• multilevel models
• modelling continuous and discrete longitudinal data
• missing data

Enroll by sending e-mail to mtt-studies@sis.uta.fi by 6.1.2015 at the latest. After this, participate to the first lecture or contact the lecturer.

Lectures and exercises on period III. Meetings on pediod IV are agreed during the course (meetings/seminars for coursework presentations).

If all the participants of the course are finnish, course is lectured in finnish.

Properties of high-dim data; Feature Selection; Linear feature extraction methods such as principal component analysis and linear discriminant analysis; Graphical excellence; Human perception; Nonlinear dimensionality reduction methods such as the self-organizing map and Laplacian embedding; Neighbor embedding methods such as stochastic neighbor embedding and the neighbor retrieval visualizer; Graph visualization; Graph layout methods such as LinLog.

Enroll by sending e-mail to the lecturer (jaakko.peltonen@uta.fi) by 6.1.2015 at the latest. After this, participate to the first lecture.

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
- M.Sc. programme in Algorithmics

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