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Arkistoitu opetusohjelma 2012–2013
Selaat vanhentunutta opetusohjelmaa. Voimassa olevan opetusohjelman löydät täältä.
MTTS1 Independent Component Analysis 5 ECTS
Periods
Period I Period II Period III Period IV
Language of instruction
English
Type or level of studies
Advanced studies
Course unit descriptions in the curriculum
Matematiikan ja tilastotieteen tutkinto-ohjelma
School of Information Sciences

General description

The independent component (IC) model is a semi-parametric multivariate model where the observable observations are considered to be linear mixtures of unobservable latent variables which have independent components. The goal of independent component analysis (ICA) is to estimate the latent variables.

In this course we will discuss the IC model and its properties as well as introduce several ICA methods.

Related models and methods will also be shortly discussed.

Requirements: Students are expected to have a basic knowledge of multivariate methods and R.

Enrolment for University Studies

Please fill the form and enroll before 7.3.2013. See the link below.

Teachers

Klaus Nordhausen, Teacher responsible
Klaus.Nordhausen[ät]uta.fi

Teaching

11-Mar-2013 – 13-May-2013
Lectures
Mon 11-Mar-2013 at 8.30-12, Pinni B0020
Mon 18-Mar-2013 - 6-May-2013 weekly at 8.30-10, Pinni B0020
Tue 19-Mar-2013 - 7-May-2013 weekly at 8.30-10, Pinni B0020
Exceptions:
16-Apr-2013 at 8.30 –10 , Exercises. Moved to Wed 17.4., Pinni B1083
23-Apr-2013 at 8.30 –10 , Exercises. Moved to Wed 24.4., Pinni B1083
Exercises
Wed 13-Mar-2013 at 10-12, Pinni B1083
Mon 18-Mar-2013 - 6-May-2013 weekly at 10-12, Pinni B1083
Exceptions:
15-Apr-2013 , Pinni B0020, Lecture
22-Apr-2013 , Pinni B0020, Lecture

Evaluation

Numeric 1-5.

Further information

No classes on week 10, nor 1.-2.4.

Course is implementation of MTTS1 Other course in Mathematics or Statistics (advanced) and can be a part of Advanced studies in Statistics.

Students of Computer Science can include course in
- M.Sc. Programme in Algorithmics and
- Specialization in Computational Methods and Programming.