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Course Catalog 2013-2014
SGN-13006 Introduction to Pattern Recognition and Machine Learning, 5 cr |
Additional information
Lectures and exercises in English.
Person responsible
Jari Niemi, Ari Visa, Joni Kämäräinen, Jussi Tohka, Ireneusz Defee, Ulla Ruotsalainen
Lessons
| Study type | P1 | P2 | P3 | P4 | Summer | Implementations | Lecture times and places |
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| Study type | Hours | Time span | Implementations | Lecture times and places |
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| SGN-13006 2013-02 |
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Requirements
Final examination and active participation in exercises.
Completion parts must belong to the same implementation
Principles and baselines related to teaching and learning
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Learning Outcomes
After completing the course, the student will know the basic structure of pattern recognition systems and the statistical bases of the classification theory (the Bayes classifier). He will distinguish supervised learning methods from the unsupervised ones. He will be able to apply supervised learning methods (model-based maximum likelihood, k-nearest neighbours) to the classifier design. The student will be able to apply k-means clustering algorithm.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | The basic structure of pattern recognition systems. Supervised and unsupervised learning. Examples of pattern recognition systems. | The design cycle of pattern recognition systems. | |
| 2. | Basics of multivariate probability and statistics, class conditional density function, Bayesian decision theory, Bayes classifier | The Bayes minimum risk classifier | |
| 3. | Parametric (model-based) and nonparametric techniques (Parzen windows, k-nearest neighbours) for supervised learning. | Maximum-likelihood | |
| 4. | Linear classifiers and regression | ||
| 5. | Validation of pattern recognition systems, cross-validation. | ||
| 6. | Algorithms for unsupervised classification. K-means clustering. | EM-algorithm |
Instructions for students on how to achieve the learning outcomes
In order to pass the course the student has to pass the exam and make at least 30% of the exercises. There will be bonus from extra exercises. To pass the exam at least half of the maximum points of the exam has to be reached. Lecture notes and exercises are enough for agood grade in exam.
Assessment scale:
Numerical evaluation scale (1-5) will be used on the course
Partial passing:
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 | No | English | ||
| Summary of lectures | Introduction to Pattern Recognition | Jussi Tohka | Yes | English |
Prerequisites
| Course | Mandatory/Advisable | Description |
| SGN-11006 Basic Course in Signal Processing | Advisable |
Additional information about prerequisites
Basics of signal processing and probability
Prerequisite relations (Requires logging in to POP)
Correspondence of content
| Course | Corresponds course | Description |
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More precise information per implementation
| Implementation | Description | Methods of instruction | Implementation |
| This course provides basic understanding of pattern recognition and machine learning methods needed for signal and data interpretation. | |||
| This course provides basic understanding of pattern recognition and machine learning methods needed for signal and data interpretation. Open university, summer teaching 30.6.-25.7.2014: Lectures: Mon-Wed 9:15-11:30 o'clock in TB219, first lecture: 30.6.2014. Exercises: 2 groups: Thu and Fri 9:15-13:00/15:00 o'clock in TC303, first exercises: 3.-4.7.2014. First exam: 28.7.2014 17-20 o'clock Course operational information will be given in the first lecture on 30.6.2014, and after that it is available in the lecture slides of the first lecture (downloadable here (see Materials) from 30.6.2014). If you have any questions, please contact via email: jari.a.niemi@tut.fi and use "SGN-13006:" in the beginning of the subject field. |