Course Catalog 2013-2014
Basic

Basic Pori International Postgraduate Open University

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Course Catalog 2013-2014

SGN-13006 Introduction to Pattern Recognition and Machine Learning, 5 cr

Additional information

Lectures and exercises in English.

Person responsible

Jari Niemi, Ari Visa, Joni Kämäräinen, Jussi Tohka, Ireneusz Defee, Ulla Ruotsalainen

Lessons

Study type P1 P2 P3 P4 Summer Implementations Lecture times and places
Lectures
Excercises
 28 h/per
 12 h/per


 


 


 


 
SGN-13006 2013-01 Tuesday 10 - 12, TB110
Wednesday 12 - 14, TB222
Monday 14 - 16, TC407

Study type Hours Time span Implementations Lecture times and places
Lectures
Excercises
36 h/time span
18 h/time span
30.06.2014 - 25.07.2014
30.06.2014 - 25.07.2014
SGN-13006 2013-02 Monday 9 - 12, TB219
Tuesday 9 - 12, TB219
Wednesday 9 - 12, TB219

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. Examples of pattern recognition systems.  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) and nonparametric techniques (Parzen windows, k-nearest neighbours) for supervised learning.    Maximum-likelihood 
4. Linear classifiers and regression     
5. Validation of pattern recognition systems, cross-validation.     
6. Algorithms for unsupervised classification. K-means clustering.    EM-algorithm 

Instructions for students on how to achieve the learning outcomes

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   No    English  
Summary of lectures   Introduction to Pattern Recognition   Jussi Tohka         Yes    English  

Prerequisites

Course Mandatory/Advisable Description
SGN-11006 Basic Course in Signal Processing Advisable    

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-13006 Introduction to Pattern Recognition and Machine Learning, 5 cr SGN-2506 Introduction to Pattern Recognition, 4 cr  

More precise information per implementation

Implementation Description Methods of instruction Implementation
SGN-13006 2013-01 This course provides basic understanding of pattern recognition and machine learning methods needed for signal and data interpretation.        
SGN-13006 2013-02 This course provides basic understanding of pattern recognition and machine learning methods needed for signal and data interpretation. Open university, summer teaching 30.6.-25.7.2014: Lectures: Mon-Wed 9:15-11:30 o'clock in TB219, first lecture: 30.6.2014. Exercises: 2 groups: Thu and Fri 9:15-13:00/15:00 o'clock in TC303, first exercises: 3.-4.7.2014. First exam: 28.7.2014 17-20 o'clock Course operational information will be given in the first lecture on 30.6.2014, and after that it is available in the lecture slides of the first lecture (downloadable here (see Materials) from 30.6.2014). If you have any questions, please contact via email: jari.a.niemi@tut.fi and use "SGN-13006:" in the beginning of the subject field.        

Last modified02.04.2014