Study unit

Show courses/exams
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 will know and master the basic methodologies of pattern recognition, and can apply them to deal with real-world pattern recognition problems. He/she is also able to independently develop new relevant applications with the help of research literature.

 
Content 

The course gives an introduction to the modern pattern recognition and machine learning methods. The selected topics of Bayesian classifiers, linear classifiers, non-linear classifiers, nearest neighbor classification methods, density estimation, k-means clustering algorithms, time series prediction, dimensionality reduction, artificial neural networks, fuzzy pattern recognition methods, and evaluation of classification and clustering results will be covered in this course.

 
Modes of study 

The course is worth 6 ECTS credits with 30 lecture hours, 14 exercise hours and examination.

 
Teaching methods 

Classroom teaching via video connection to Joensuu campus

 
Study materials 

Konstantinos Koutroumbas and Sergios Theodoridis: Pattern Recognition, Elsevier Academic Press, 2nd ed. (selected chapters); 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; lecture slides and notes; selected papers from journals and conference proceedings

 
Evaluation criteria 

Examination (80%) and assignments/computer exercises (20%) are used for evaluation. The final grade of this course is from 0 to 5.

 
Prerequisites 

No formal prerequisites are needed, but good programming skills as well as familiarity with basic mathematics, such as linear algebra, will be helpful.

 
Time 

Spring semester

 
Offering data 

Computer Science M.Sc students, Computer Science international M.Sc students, Computer Science exchange students, students who have completed basic and intermediate studies in Computer Science.

 
Further information 

Teaching in English

 


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 (JOENSUU): Pattern Recognition  Lecture and exercise course  Xiaozhi Gao  21.03.22 -17.06.22
Registration not started (KUOPIO): Pattern Recognition  Lecture and exercise course  Xiaozhi Gao  23.03.22 -17.06.22

Future exams
No exams