Study unit

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3621517 Pattern Recognition, 5-6 Cp 
Code 3621517  Validity 01.05.2010 -
Name Pattern Recognition  Abbreviation HAT 
Credits5-6 Cp  Date of expiry  
TypeAdvanced Studies Subject0530 Computer Science 
ClassStudy Unit  Hours  
Study right   Grading0-5 
Recommended scheduling 
   
Organisation Computer Science (J,K) 

Description
Learning outcomes 

After the course, the student knows the basic methodology of pattern recognition and can apply it into a real world pattern recognition problem. He/she is also able to develop new applications with the help of research literature.

 
Content 

The course gives an introduction to modern pattern recognition and machine learning methods. Classification, Bayesian classifier, linear classifiers, non-linear classifiers, nearest neighbor methods, density estimation, clustering, dimensionality reduction, artificial neural networks, evaluation of classification and clustering results.

 
Modes of study 

final exam and practical exercises

 
Teaching methods 

Lectures 28 h, excercises 14 h and practical exercises

 
Study materials 

Theodoridis – Koutroumbas: Pattern Recognition, Elsevier Academic Press, 2nd ed. or later. Selected chapters. Lisäksi valittuja osia teoksista Duda, Hart & Stork: Pattern Classification 2nd ed., Wiley 2000; Christopher Bishop: Pattern Recognition and Machine Learning, Springer, 2006; Aaron Courville, Ian Goodfellow & Yoshua Bengio:

Deep Learning, MIT Press 2016

 
Evaluation criteria 

0-5

 
Prerequisites 

No formal prerequisites but good programming skills as well as familiarity with basic mathematical tools (in specific, linear algebra) will be helpful.

 


Letter (J, K) in front of the name of the course/exam indicates the campus on which teaching or exam takes place: J = Joensuu, K = Kuopio.

Present and future teaching
Functions Name Type Cp Teacher Timetable
Registration not started (KUOPIO): Pattern Recognition  Lecture and exercise course  Xiaozhi Gao  18.03.20 -16.06.20
Registration not started (JOENSUU): Pattern Recognition  Lecture and exercise course  Xiaozhi Gao  18.03.20 -16.06.20

Future exams
No exams