Opinto-opas 2005-2006

SGN-2556 PATTERN RECOGNITION, 5 cr
PATTERN RECOGNITION

Person responsible
Prof. Ari Visa and Docent Ulla Ruotsalainen

Lecturers
Ulla Ruotsalainen, Docent, room TE311, ulla.ruotsalainen@tut.fi
Ari Visa, Professor, room TF309, ari.visa@tut.fi

Lecture room and time
Per V: Tuesday 12 - 14, TB219
Per V: Thursday 12 - 14, TB223

Implementation rounds
Implementation 1
  Period 1 Period 2 Period 3 Period 4 Period 5 Summer Language of instruction
Lecture - - - - 4 h/week - In English only
Exercise - - - - 4 h/week - In English only
Exam   In English only
(Academic Calender 2005-2006)

Objectives
The aim is 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.

Contents
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    

Requirements for completing the course
Exam and Matlab exercises. The exercises are mandatory.

Assessment criteria
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.

  • Used assessment scale is passed / failed

    • Study material
    Type Name Author ISBN URL, edition, availablitity... Exam material Language
    Book "Pattern Classification" Duda RO, Hart PE, Stork DG   2nd edition, Wiley, 2001 Yes  English 

    Prerequisites
    Number Name Credits M/R
    SGN-2500 Introduction to Pattern Recognition 4 Mandatory
    SGN-2506 Introduction to Pattern Recognition 4 Mandatory

    Additional information related to prequisites
    Either SGN-2500 or SGN-2506 is required.

    Other comments

  • The course is suitable for postgraduate studies.

    • Correspondence of content
    8002303 Pattern Recognition

    Course homepage

    Last modified 06.03.2006
    Modified byAntti Niemistö