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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., 14-11-2016 through 3-2-2017; retake week 16)
Participants:up till now 68 subscriptions
Schedule:Official schedule representation can be found in Osiris
Teachers:Dit is een oud rooster!
lab session group 1 Wed 13.15-15.0050 BBG-112 CLZ
Fri 11.00-12.4547-49 BBG-106 CLZ
51 BBG-106 CLZ
2-4 BBG-106 CLZ
group 2 Wed 13.15-15.0050 BBG-109 CLZ
Fri 11.00-12.4547-49 BBG-103 CLZ
51 BBG-103 CLZ
2-4 BBG-103 CLZ
lecture   Wed 15.15-17.0046-51 BBG-023 Ad Feelders
Marc van Kreveld
2-4 BBG-023
Fri 13.15-15.0047-49 BBG-161
51 BBG-161
2-4 BBG-161
week: 5Wed 30-1-201913.30-16.30 uurroom: EDUC-THEATRON
week: 16Wed 17-4-201913.30-16.30 uurroom: OLYMPOS-HAL2retake exam
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 (first 5 weeks) 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.
  • Support vector machines.
  • Neural networks
  • Unsupervised learning/Clustering.

The subjects treated in the part on geometrical pattern recognition (last four weeks) are:

  • Point patterns: matching, similarity, fitting to models.
  • Patterns in polygonal curves, polygons, trajectories, and other geometric data.
  • Similarity measures, metrics, outliers.
The small project concerns applications of geometric pattern recognition and involves literature search, surveying, brainstorming, and a short document (at most four pages).

Knowledge of elementary probability theory, algorithms, statistics, and linear algebra is presupposed.
  • Gareth James et al., An Introduction to Statistical Learning with applications in R, Springer, 2013.
  • Trevor Hastie et al., The Elements of Statistical Learning (2nd edition), Springer, 2009.
  • Papers to be made available during the course.
Course form:Lectures, computer lab, and small research project.
Exam form:Written Exam, Practical Assignments, and small project
Minimum effort to qualify for 2nd chance exam:Om aan de aanvullende toets te mogen meedoen moet de oorspronkelijke uitslag minstens 4 zijn.