Department of Information and Computing Sciences

Departement Informatica Onderwijs
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Onderwijs Informatica en Informatiekunde

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Pattern recognition

Te lang geleden voor docent- en roosterinformatie
Website:website containing additional information
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, model selection, regularization, the curse of dimensionality.
  • Linear statistical models for regression and classification.
  • Neural networks.
  • Support vector machines.
  • 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.
  • Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer 2006.
  • Gareth James et al., An Introduction to Statistical Learning with applications in R, Springer, 2013.
  • 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.