# Department of Information and Computing Sciences

## Onderwijs Informatica en Informatiekunde

Vak-informatie Informatica en Informatiekunde

# Pattern recognition

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 80 subscriptions
Schedule:Official schedule representation can be found in Osiris
Teachers:Dit is een oud rooster!
formgrouptimeweekroomteacher
lab session          studentassistent SB
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
50 RUPPERT-ROOD
2-4 RUPPERT-A
Exam:
 week: 16 Wed 17-4-2019 13.30-16.30 uur room: BBG-205 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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