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Pattern recognition

Website:website containing additional information
Course code:INFOMPR
Credits:7.5 ECTS
Period:period 2 (week 46 through 5, i.e., 12-11-2018 through 1-2-2019; retake week 16)
Participants:up till now 85 subscriptions
Schedule:Official schedule representation can be found in Osiris
lab session group 1 Fri 11.00-12.4547-51 KBG-228
2-4 KBG-228
group 2 Fri 11.00-12.4547-51 BBG-103 CLZ studentassistent SL
2-4 BBG-103 CLZ
group 3 Fri 11.00-12.4547-51 BBG-175 CLZ
2-4 BBG-175 CLZ
lecture   Wed 15.15-17.0046-51 RUPPERT-A Ad Feelders
Zerrin Yumak
Fri 13.15-15.0047-51 RUPPERT-A
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
  • 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: