Onderwijs Informatica en Informatiekunde

Vak-informatie Informatica en Informatiekunde

Pattern recognition

Te lang geleden voor docent- en roosterinformatie

Website:

website containing additional information

Exam:

week: 16

Wed 17-4-2019

13.30-16.30 uur

room: OLYMPOS-HAL2

retake 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.

Literature:

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.

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Department of Information and Computing Sciences

Onderwijs Informatica en Informatiekunde

Pattern recognition