Course Catalog 2011-2012
International

Basic Pori International Postgraduate Open University

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Course Catalog 2011-2012

SGN-2506 Introduction to Pattern Recognition, 4 cr

Additional information

Lectures and exercises in English.

Person responsible

Jussi Tohka, Ulla Ruotsalainen

Lessons

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


 
 28 h/per
 12 h/per


 


 


 
SGN-2506 2011-01 Wednesday 12 - 14, TB223
Thursday 10 - 12, TB223

Study type Hours Time span Implementations Lecture times and places
Lectures
Excercises
32 h/time span
16 h/time span
02.07.2012 - 27.07.2012
02.07.2012 - 27.07.2012
SGN-2506 2011-02 Monday 10 - 14, TB222
Tuesday 10 - 14, TB222

Requirements

Final examination and active participation in exercises.
Completion parts must belong to the same implementation

Principles and baselines related to teaching and learning

-

Learning outcomes

After completing the course, the student will know the basic structure of pattern recognition systems and the statistical bases of the classification theory (the Bayes classifier). He will distinguish supervised learning methods from the unsupervised ones. He will be able to apply supervised learning methods (model-based maximum likelihood, k-nearest neighbours) to the classifier design. The student will be able to apply k-means clustering algorithm.

Content

Content Core content Complementary knowledge Specialist knowledge
1. The basic structure of pattern recognition systems. Supervised and unsupervised learning.   The design cycle of pattern recognition systems.   
2. Basics of multivariate probability and statistics, class conditional density function, Bayesian decision theory, Bayes classifier  The Bayes minimum risk classifier   
3. Parametric (model-based maximum likelihood) and nonparametric techniques (Parzen windows, k-nearest neighbours) for the estimation of density functions and the design of pattern classifiers based on training data.     
4. Linear classifier, Perceptron algorithm  Minimum squared error method   
5. Testing of pattern recognition systems.     
6. Algorithms for unsupervised classification. K-means clustering.    EM-algorithm 

Evaluation criteria for the course

In order to pass the course the student has to pass the exam and make at least 30% of the exercises. There will be bonus from extra exercises. To pass the exam at least half of the maximum points of the exam has to be reached. Lecture notes and exercises are enough to good grade in exam.

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  
Summary of lectures   "Introduction to Pattern Recognition"   Jussi Tohka            English  

Additional information about prerequisites
Basics of signal processing and probability

Prerequisite relations (Requires logging in to POP)



Correspondence of content

Course Corresponds course  Description 
SGN-2506 Introduction to Pattern Recognition, 4 cr 8001652 Introduction to Pattern Recognition, 2 cu  
SGN-2506 Introduction to Pattern Recognition, 4 cr SGN-2500 Introduction to Pattern Recognition, 4 cr  

More precise information per implementation

Implementation Description Methods of instruction Implementation
SGN-2506 2011-01        
SGN-2506 2011-02 Teaching: 32 hours lectures (non-mandatory), 16 hours exercises (30% mandatory). No particular exercise session is mandatory although in some of them it might be stated so in POP. The contents and requirements are the same as in the previous implementation of SGN-2506 in Fall 2011. Detailed information for the summer implementation is available on the course web page: http://www.cs.tut.fi/kurssit/SGN-2506/        

Last modified28.03.2012