Course Catalog 2012-2013
Open University

Basic Pori International Postgraduate Open University

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Course Catalog 2012-2013

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


 


 


 


 
 32 h/per
 16 h/per
SGN-2506 2012-02 Monday 10 - 14, TB222
Tuesday 10 - 14, TB222
Wednesday 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 for agood 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 SGN-13006 Introduction to Pattern Recognition and Machine Learning, 5 cr  
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 2012-02 Summer course taught by Jari Niemi. Lectures (32h) and exercises (16h, choose either Thursday's or Friday's group below) are in English. All material is in English. Exam problems are in English. More detailed operational information will be given in the first lecture on Jul 1 2013 or via email (jari.a.niemi@tut.fi) on request. It is highly recommended that everyone takes part in the first lecture. If that is not possible, please contact via email (jari.a.niemi@tut.fi) at the beginning of the course.       Contact teaching: 0 %
Distance learning: 0 %
Self-directed learning: 0 %  

Last modified28.03.2013