autumn 2024
FYS-3012 Pattern recognition - 10 ECTS
Course content
The course covers data analysis techniques such as Bayes classifiers, estimation of probability density functions and related non-parametric classification approaches. Further, linear classifiers using least squares are addressed, in addition to simple processing units (neurons) and their extension to artificial neural networks. Linear and nonlinear (using kernel functions) support vector machine classifiers are discussed, in addition to feature extraction and data transformation using eigenvector-based methods such as Fisher discriminants. Methods for grouping, or clustering, data are treated in detail, including hierarchical clustering and k-means. Exercises and problem solving, in addition to practical pattern recognition for data analysis using programming, are strongly emphasized. Basic programming skills are required.Objectives of the course
Knowledge - The student can
- describe the concepts of classification, clustering and dimensionality reduction in data analysis
- apply advanced methods from pattern recognition to diverse classification, dimensionality reduction and clustering problems
- compare different algorithms with respect to their strengths and applicability
- describe important pattern recognition applications in society
Skills - The student can
- analyse modern pattern recognition methods and apply them in independent practical and theoretical problem solving
- train and validate a pattern recognition algorithm for a defined task using available data
- analyse Bayes classifiers in terms of error probabilities
- design linear classifiers for minimization of squared errors and other criteria
- design and analyse nonlinear classifiers in the form of neural networks
- perform feature extraction and data transformation, e.g. using eigenvectors
- explain different clustering algorithms, and analyse their strengths
- implement in practice all methods discussed in the course for analysis of data
General competence - The student can
- report the comparison of several pattern recognition algorithms applied to a practical problem
- appreciate the importance of pattern recognition in society
- work with pattern recognition methods for analysis of real data
Information to incoming exchange students
This course is open for inbound exchange student who meets the admission requirements. Please see the Admission requirements’ section. Do you have questions about this module? Please check the following website to contact the course coordinator for exchange students at the faculty: https://en.uit.no/education/art?p_document_id=510412Schedule
Examination
| Examination: | Weighting: | Duration: | Grade scale: |
|---|---|---|---|
| Off campus exam | 5/10 | 2 Weeks | A–E, fail F |
| School exam | 5/10 | 4 Hours | A–E, fail F |
- About the course
- Campus: Tromsø |
- ECTS: 10
- Course code: FYS-3012
- Responsible unit
- Institutt for fysikk og teknologi
- Kontaktpersoner
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- Tidligere år og semester for dette emnet