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Course Catalog 2014-2015
SGN-41006 Signal Interpretation Methods, 4 cr |
Additional information
Suitable for postgraduate studies
Person responsible
Katariina Mahkonen, Jari Niemi, Joni Kämäräinen, Jussi Tohka
Lessons
| Study type | P1 | P2 | P3 | P4 | Summer | Implementations | Lecture times and places |
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Requirements
Exam, homeworks and exercises.
Completion parts must belong to the same implementation
Learning Outcomes
Students understand principles of selected pattern recognition and machine learning approaches for interpreting signals. Student can apply the methods to real problems.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | Probabilistic formulation of decision theory; The Bayes classifier | Neyman-Pearson decision rule | |
| 2. | Plug-in classifiers (Gaussian, density estimation, k-nearest neighbours) | Gaussian mixtures and EM algorithm | |
| 3. | Linear and kernel models for regression and classification, support vector machines, regularisation | Radial basis function networks | Support vector machines as regularisation method |
| 4. | Ensemble methods (Random forests, Bagging, Boosting, Stacking) | Model averaging | |
| 5. | Performance evaluation , no free lunch theorem, comparing classifiers | ||
| 6. | Feature extraction and selection | ||
| 7. | Unsupervised learning, clustering |
Instructions for students on how to achieve the learning outcomes
Accepted exercises and homeworks. Final 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 | Elements of Statistical Learning: Data Mining, Inference, and Prediction | T. Hastie, R. Tibshirani and J. Friedman | Some topics are not dealt in sufficient detail in the main text. Supplementary material will be taken from this book. | Yes | English | ||
| Book | Machine Learning | Tom M. Mitchell | 0-07-042807-7 | No | English | ||
| Book | Pattern Recognition and Machine Learning | Christopher M. Bishop | 0-387-31073-8 | No | English | ||
| Book | Statistical Pattern Recognition | Andrew R. Webb and Keith D. Copsey | This is the main text for the course. A | Yes | English |
Prerequisites
| Course | Mandatory/Advisable | Description |
| SGN-13000 Johdatus hahmontunnistukseen ja koneoppimiseen | Mandatory | |
| SGN-13006 Introduction to Pattern Recognition and Machine Learning | Mandatory |
Additional information about prerequisites
Good programming skills in general, and basic skills on the Matlab environment are required.
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 |
| MSc level course on pattern recognition and machine learning methods and approaches used in signal interpretation. The aim of the course is to provide ability to apply PR and ML methods in students' own system development work. The practical exercises (Matlab) are essential part of the course giving the possibility to utilize the methods in practical problems. |