|Period:||period 2 (week 46 through 5, i.e., 12-11-2018 through 1-2-2019; retake week 16)
|Participants:||up till now 40 subscriptions|
|Schedule:||Official schedule representation can be found in Osiris|
|week: 5||Wed 30-1-2019||13.30-16.30 uur||room: EDUC-THEATRON|
|week: 16||Wed 17-4-2019||13.30-16.30 uur||room: -||retake exam|
|Contents:||For questions about enrollment, registration, waiting lists,
admittance, etc. please contact the student desk at firstname.lastname@example.org.
In this course we study statistical pattern recognition and machine learning.
The subjects covered are:
Knowledge of elementary probability theory, statistics,
multivariable calculus and linear algebra is presupposed.
- General principles of data analysis: overfitting, bias-variance trade-off, model selection, regularization, the curse of dimensionality.
- Linear statistical models for regression and classification.
- Clustering and unsupervised learning.
- Support vector machines.
- Neural networks and deep learning.
- Book: Christopher M. Bishop, Pattern Recognition and Machine Learning,
- Additional literature for the most recent material in the form of research papers, book chapters, etc.
|Course form:||Lectures and computer lab sessions.|
|Exam form:||Written exam and practical assignments.|
|Minimum effort to qualify for 2nd chance exam:|| |