|Period:||period 2 (week 46 through 5, i.e., 11-11-2013 through 31-1-2014; retake week 11)
|Participants:||up till now 34 subscriptions|
|Schedule:||Official schedule representation can be found in Osiris|
|Teachers:||Dit is een oud rooster!
|Contents:||In this course we study statistical pattern recognition as well as
geometric pattern recognition.
The subjects treated in the part on statistical pattern recognition are:
Knowledge of elementary probability theory, statistics and linear algebra
- General principles of data analysis: overfitting, model selection, regularization, the curse of dimensionality.
- Linear statistical models for regression and classification
- Neural networks
- Support vector machines
|Literature:||Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer 2006.|
|Course form:||Lectures and Computer Lab|
|Exam form:||Written Exam and Practical Assignments|
|Minimum effort to qualify for 2nd chance exam:|| |