SGN-13006 Introduction to Pattern Recognition and Machine Learning, 5 cr
Additional information
Lectures and exercises in English.
Person responsible
Joni Kämäräinen
Lessons
| Implementation | Period | Person responsible | Requirements |
| SGN-13006 2016-01 | 1 |
Dingding Cai Junsheng Fu Joni Kämäräinen Fatemeh Shokrollahi Yancheshmeh |
Final examination and exercises. |
Learning Outcomes
The student understands the main concepts and fundamental approaches in pattern recognition and machine learning. 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 the principal idea of 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. | Pattern recognition and machine learning in robotics and re-inforcement learning. |
Instructions for students on how to achieve the learning outcomes
You must actively participate the lectures and do the exercises. In particular, familiarize yourself with the exercise questions before the exercise session.
Assessment scale:
Numerical evaluation scale (0-5)
Partial passing:
Study material
| Type | Name | Author | ISBN | URL | Additional information | Examination material |
| 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 | ||
| Book | Machine Learning | Tom Mitchell | 0070428077 | Contents of many lectures follow this book | Yes | |
| 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 |
Additional information about prerequisites
No mandatory requirements, but it is assumed that the student has good knowledge of BSc level engineering mathematics and programming.
Correspondence of content
| Course | Corresponds course | Description |
| SGN-13006 Introduction to Pattern Recognition and Machine Learning, 5 cr | SGN-2506 Introduction to Pattern Recognition, 4 cr | |
| SGN-13006 Introduction to Pattern Recognition and Machine Learning, 5 cr | SGN-13000 Introduction to Pattern Recognition and Machine Learning, 5 cr |