Course - Pattern Recognition - TT8001
TT8001
Pattern Recognition
Lessons are not given in the academic year 2010/2011
Credits
7.5
Level
Doctoral degree level
Examination arrangement
Oral examination
About
About the course
Course content
The course concerns statistical methods for classification and clustering. Within classification focus is set on subjects like Bayesian theory, parametric and non-parametric techniques, different estimation methods, distortion measures, different classifier structures, static and dynamic problems, etc. Within clustering focus is set upon hierarchical methods, classical algorithms like K-means, newer methods like fuzzy and competitive methods, etc.
Recommended previous knowledge
Knowledge of basic statistics, estimation theory and vector algebra
Required previous knowledge
Knowledge minimum comparable to course TMA4245 Statistics.
Course materials
Course material is announced at course start.
Subject areas
- Signal Processing
- Statistics
- Engineering Cybernetics
Contact information
Department with academic responsibility
Examination
Examination
Examination arrangement: Oral examination
Grade: Letters
Ordinary examination - Autumn 2010
Oral examination
Weighting
100/100