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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., 11-11-2019 through 31-1-2020; retake week 16)
Timeslot:D
Participants:up till now 5 subscriptions
Schedule:Official schedule representation can be found in Osiris
Teachers:
formgrouptimeweekroomteacher
lecture   Wed 15.15-17.0046-51 RUPPERT-A Ad Feelders
Zerrin Yumak
2-4 RUPPERT-A
Fri 15.15-17.0046 BOL-0.204
47-51 RUPPERT-A
2-4 RUPPERT-A
tutorial group 1 Fri 13.15-15.0046 BOL-3.108
47-51 RUPPERT-111
2-5 RUPPERT-111
group 2 Fri 13.15-15.0046 HFG-611AB
47-51 HFG-611AB
2-5 HFG-611AB
Exam:
week: 5Fri 31-1-202017.00-20.00 uurroom: EDUC-BETA
Note:No up-to-date course description available.
Text below is from year 2018/2019
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 science.gsns@uu.nl.
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: http://www.deeplearningbook.org.
  • 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.
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