Department of Information and Computing Sciences

Departement Informatica Onderwijs
Bachelor Informatica Informatiekunde Kunstmatige intelligentie Master Computing Science Game&Media Technology Artifical Intelligence 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:periode 2 (week 46 t/m 5, dwz 14-11-2016 t/m 3-2-2017; herkansing week 16)
Timeslot:D
Participants:up till now 68 subscriptions
Schedule:Official schedule representation can be found in Osiris
Teachers:
formgrouptimeweekroomteacher
college   wo 15.15-17.0046-51 BBG-023 Ad Feelders
Marc van Kreveld
   
2-4 BBG-023
vr 13.15-15.0047-49 BBG-161
51 BBG-161
2-4 BBG-161
practicum groep 1 wo 13.15-15.0050 BBG-112 CLZ
vr 11.00-12.4547-49 BBG-106 CLZ
51 BBG-106 CLZ
2-4 BBG-106 CLZ
groep 2 wo 13.15-15.0050 BBG-109 CLZ
vr 11.00-12.4547-49 BBG-103 CLZ
51 BBG-103 CLZ
2-4 BBG-103 CLZ
Tentamen:
week: 16vr 21-4-201713.30-16.30 uurzaal: BBG-223aanvullende toets
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:
  • 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.
wijzigen?