Course Catalog 2010-2011
International

Basic Pori International Postgraduate Open University

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Course Catalog 2010-2011

SGN-2556 Pattern Recognition, 5 cr

Person responsible

Ari Visa, Jussi Tohka, Ulla Ruotsalainen

Lessons

Study type P1 P2 P3 P4 Summer Implementations Lecture times and places
Lectures
Excercises


 


 
 24 h/per
 24 h/per


 


 
SGN-2556 2010-01 Tuesday 10 - 12, S3
Thursday 10 - 12, S3

Requirements

Exam and Matlab exercises. The exercises are mandatory.

Principles and baselines related to teaching and learning

-

Learning outcomes

The aim is to deepen the understanding of pattern recognition principles and give students some ability to apply the methods on real problems. The aim is also to learn how to write in a scientific publication about the methods and the pattern classification results.

Content

Content Core content Complementary knowledge Specialist knowledge
1. Bayesian decision theory and Bayesian parameter estimation.  Belief networks, Hidden Markov models, Linear discriminant functions   
2. Stochastic pattern classification methods.  Boltzman learning, Evolutionary methods, Genetic programming   
3. Nonmetric classification methods.  CART, tree methods in principle, Grammatical methods   
4. Algorithm-independent machine learning.     
5. Unsupervised learning and clustering, fuzzy clustering methods, component analysis methods.  Mixture densities, Hierarchical clustering, on-line clustering, graph theoretic methods, PCA and ICA   

Evaluation criteria for the course

In order to pass the course the student has to complete all the exercises and get half of the maximum points from the exam. Grading is pass/fail.

Assessment scale:

Numerical evaluation scale (1-5) will be used on the course

Study material

Type Name Author ISBN URL Edition, availability, ... Examination material Language
Book   "Pattern Classification"   Duda RO, Hart PE, Stork DG       2nd edition, Wiley, 2001      English  

Prerequisite relations (Requires logging in to POP)



Correspondence of content

Course Corresponds course  Description 
SGN-2556 Pattern Recognition, 5 cr 8002303 Pattern Recognition, 3 cu  

Additional information

Suitable for postgraduate studies

More precise information per implementation

Implementation Description Methods of instruction Implementation
SGN-2556 2010-01 Postgraduate course on pattern recognition to deepen the knowledge of pattern recognition methods. The aim of the course is to provide ability to apply the methods in student's own research work. The Matlab exercises are essential part of the course giving the possibility to utilize the methods in practical problems.        

Last modified21.12.2010