Department of Information and Computing Sciences

Departement Informatica Onderwijs
Bachelor Informatica Informatiekunde Kunstmatige intelligentie Master Computing Science Game&Media Technology Artifical Intelligence Human Computer Interaction Business Informatics

Onderwijs Informatica en Informatiekunde

Vak-informatie Informatica en Informatiekunde

Pattern recognition

Website:website containing additional information
Course code:INFOMPR
Credits:7.5 ECTS
Period:period 2 (week 46 through 5, i.e., 9-11-2020 through 5-2-2021; retake week 16)
Participants:up till now 129 subscriptions
Schedule:Official schedule representation can be found in MyTimetable
lecture          Ad Feelders
Zerrin Yumak
tutorial group 1        Jiayuan Hu
group 2        Ali Katsheh
Note:No up-to-date course description available.
Text below is from year 2019/2020
Contents:Knowledge of elementary probability theory, statistics, multivariable calculus and linear algebra is presupposed.

In this course we study statistical pattern recognition and machine learning.

The subjects covered are:

  • 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.
For questions about enrollment, registration, waiting lists, admittance, etc. please contact the student desk at
Literature:May change!
  • Book: Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
  • Book: Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2016.
    Book URL:
  • Possibly additional literature 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:To qualify for the retake exam, the grade of the original must be at least 4.