|
|
|||||||||||||||||
Course Catalog 2011-2012
SGN-2556 Pattern Recognition, 5 cr |
Additional information
Suitable for postgraduate studies
Person responsible
Ari Visa, Jussi Tohka, Ulla Ruotsalainen
Lessons
| Study type | P1 | P2 | P3 | P4 | Summer | Implementations | Lecture times and places |
|
|
|
|
|
|
|
|
|
Requirements
Exam and Matlab exercises. The exercises are mandatory.
Principles and baselines related to teaching and learning
-
Learning outcomes
The aim is to deepen the understanding of pattern recognition principles and give students some ability to apply the methods on real problems. The aim is also to learn how to write in a scientific publication about the methods and the pattern classification results.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | Bayesian decision theory and Bayesian parameter estimation. | Belief networks, Hidden Markov models, Linear discriminant functions | |
| 2. | Stochastic pattern classification methods. | Boltzman learning, Evolutionary methods, Genetic programming | |
| 3. | Nonmetric classification methods. | CART, tree methods in principle, Grammatical methods | |
| 4. | Algorithm-independent machine learning. | ||
| 5. | Unsupervised learning and clustering, fuzzy clustering methods, component analysis methods. | Mixture densities, Hierarchical clustering, on-line clustering, graph theoretic methods, PCA and ICA |
Evaluation criteria for the course
In order to pass the course the student has to complete all the exercises and get half of the maximum points from the exam. Grading is pass/fail.
Assessment scale:
Numerical evaluation scale (1-5) will be used on the course
Study material
| Type | Name | Author | ISBN | URL | Edition, availability, ... | Examination material | Language |
| Book | "Pattern Classification" | Duda RO, Hart PE, Stork DG | 2nd edition, Wiley, 2001 | English |
Prerequisite relations (Requires logging in to POP)
Correspondence of content
| Course | Corresponds course | Description |
|
|
|
More precise information per implementation
| Implementation | Description | Methods of instruction | Implementation |
| Postgraduate course on pattern recognition to deepen the knowledge of pattern recognition methods. The aim of the course is to provide ability to apply the methods in student's own research work. The Matlab exercises are essential part of the course giving the possibility to utilize the methods in practical problems. |