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Course Catalog 2014-2015
SGN-13006 Introduction to Pattern Recognition and Machine Learning, 5 cr |
Additional information
Lectures and exercises in English.
Person responsible
Joni Kämäräinen
Lessons
| Study type | P1 | P2 | P3 | P4 | Summer | Implementations | Lecture times and places |
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Requirements
Final examination and exercises.
Completion parts must belong to the same implementation
Learning Outcomes
The student understands the main concepts and fundamental approaches in pattern recognition and machine learning. Most main approaches will be covered and their strengths and weaknesses discussed. After this course the student is able to study more advanced topics and courses in pattern recognition and machine learning. Students will also be able to implement basic methods and utilise existing software packages and libraries of machine learning.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | Basic work flow in pattern recognition and machine learning. Linear models of regression and classification as the starting point. | ||
| 2. | Concept learning. | ||
| 3. | Decision tree learning | Random forests | |
| 4. | Bayesian learning and probability density estimation | ||
| 5. | Prolog language and inductive logic programming. | ||
| 6. | Multi-layer perception neural networks and support vector machines. | ||
| 7. | Unsupervised learning including clustering, self-organising map and linear methods (principal component analysis) | ||
| 8. | Instance-based learning. | ||
| 9. | Pattern recognition and machine learning in robotics and re-inforcement learning. |
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 the exercises.
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 | Elements of Statistical Learning, 2nd edition | Trevor Hastie, Robert Tibshirani, Jerome Friedman | Covers all the required methods, but is rather statistical approach. Mainly the random forest part is taken from this book. | Yes | English | ||
| Book | Machine Learning | Tom Mitchell | 0070428077 | Contents of many lectures follow this book | Yes | English | |
| Book | Statistical Pattern Recognition, 3rd Edition | Andrew R. Webb, Keith D. Copsey | 978-0-470-68227-2 | Very good book about the topic from practioners. Mainly the support vector machines part is taken from this book. | Yes | English |
Additional information about prerequisites
No mandatory requirements, but it is assumed that a student has good knowledge of BSc level engineering mathematics and programming.
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 |
| Summer implementation of SGN-13006. Course contents and exam requirements are same as in the implementation of Period 1 of 2014. However, certain practicalities differ. Detailed operational information is given in the slides of the first lecture. |