Course Catalog 2006-2007

SGN-2556 PATTERN RECOGNITION, 5 cr
Pattern Recognition

Courses persons responsible
Ulla Ruotsalainen
Ari Visa

Lecturers
Ulla Ruotsalainen
Ari Visa

Lecturetimes and places
Per V: Tuesday 10 - 12, TB214
Per V: Thursday 10 - 12, TB214

Implementations
  Period 1 Period 2 Period 3 Period 4 Period 5 Summer
Lecture - - - - 4 h/week -
Exercise - - - - 4 h/week -
Exam  
(Timetable for academic year 2006-2007)

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.

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    

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

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.

  • Used assessment scale is numeric (1-5)

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

    Prerequisites
    Code Course Credits M/R
    SGN-2500 SGN-2500 Introduction to Pattern Recognition 4 Mandatory
    SGN-2506 SGN-2506 Introduction to Pattern Recognition 4 Mandatory

    Prequisite relations (Sign up to TUT Intranet required)

    Additional information about prerequisites
    Either SGN-2500 or SGN-2506 is required.

    Remarks

  • The course is suitable for postgraduate studies.

    • Scaling
    Methods of instructionHours
    Lectures 48
    Exercises 60

    Other scaledHours
    Preparation for exam 20
    Exam/midterm exam 3
    Total sum 131

    Principles and starting points related to the instruction and learning of the course

  • The course is based on lectures and exercises. During these the students are encouraged to present their own research problems and questions related to the pattern classification. The exercise tasks include reporting of the methods and results as it would be done in a scientific publication.

    • Additional information related to course

    Correspondence of content
    8002303 Pattern Recognition

    Course homepage

    Last modified 27.03.2007
    Modified byKirsi Järnström