Course Catalog 2012-2013
Basic

Basic Pori International Postgraduate Open University

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Course Catalog 2012-2013

SGN-2556 Pattern Recognition, 5 cr

Additional information

Suitable for postgraduate studies

Person responsible

Juho Vihonen, Ari Visa, Joni Kämäräinen, Jussi Tohka, Ulla Ruotsalainen

Lessons

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


 


 
 4 h/week
 4 h/week


 


 
SGN-2556 2012-01 Tuesday 10 - 12, TB222
Thursday 10 - 12, TB223
Wednesday 10 - 12, TB224
Thursday 10 - 12, S3

Requirements

Exam and Matlab exercises. The exercises are mandatory.
Completion parts must belong to the same implementation

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 (graphical models). Linear and generalized linear discriminant functions  Hidden Markov models   
2. Stochastic pattern classification methods; Boltzman learning, Evolutionary methods in optimization  Genetic programming   
3. Nonmetric classification methods: CART, tree methods in principle.  Grammatical methods   
4. Algorithm-independent machine learning. No free lunch theorem, cross-validation, bootstrap, bagging and boosting      
5. Unsupervised learning and clustering, fuzzy clustering methods, component analysis methods.Mixture densities, Hierarchical clustering, PCA and ICA  on-line clustering, graph theoretic methods,   

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

Partial passing:

Completion parts must belong to the same implementation

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  

Prerequisites

Course Mandatory/Advisable Description
SGN-2500 Johdatus hahmontunnistukseen Mandatory   1
SGN-2506 Introduction to Pattern Recognition Mandatory   1

1 . Either course SGN-2500 or SGN-2506 or corresponding knowledge is required

Additional information about prerequisites
Good Matlab programming skills are required; Experience in academic/scientific writing in English is required (e.g. Bachelor or Master thesis)

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  
SGN-2556 Pattern Recognition, 5 cr SGN-41006 Signal Interpretation Methods, 4 cr  

More precise information per implementation

Implementation Description Methods of instruction Implementation
SGN-2556 2012-01 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 modified09.01.2013