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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., 13-11-2017 through 2-2-2018; retake week 16)
Timeslot:D
Participants:up till now 7 subscriptions
Schedule:Official schedule representation can be found in Osiris
Teachers:
formgrouptimeweekroomteacher
lab session group 1 Fri 11.00-12.4547-51 BBG-112 CLZ
2-4 BBG-112 CLZ
group 2 Fri 11.00-12.4547 BBG-109 CLZ
48 BBG-065
49 BBG-109 CLZ
50 BBG-209
51 BBG-065
2-4 BBG-065
group 3 Fri 11.00-12.4547 BBG-109 CLZ
48 BBG-065
49 BBG-109 CLZ
50 BBG-209
51 BBG-065
2-4 BBG-065
lecture   Wed 15.15-17.0046-51 RUPPERT-A Ad Feelders
Marc van Kreveld
   
2-4 RUPPERT-A
Fri 13.15-15.0047-51 RUPPERT-A
2-4 RUPPERT-A
Exam:
week: 5Fri 2-2-201813.30-16.30 uurroom: EDUC-THEATRON
week: 16Fri 20-4-201813.30-16.30 uurroom: -retake exam
Note:No up-to-date course description available.
Text below is from year 2016/2017
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.
Literature:May change!
  • 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:To qualify for the retake exam, the grade of the original must be at least 4.
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