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Course Catalog 2013-2014
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
Principles and baselines related to teaching and learning
Students are strongly recommended to participate the lectures and exercises as many topics are discussed in detail and interactively using black board and lecturer's notes. During the exercise sessions we code and students test their methods using real data.
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. | Historical perspective to signal interpretation using pattern recognition and machine learning (concept learning, expert systems etc.) Practical application examples. | ||
| 2. | Decision tree learning and random forests. | Bagging and bootstrapping. | |
| 3. | Bayesian decision making and learning. | Bayesian Belief Networks. Structured learning. | Non-Bayesian tasks. |
| 4. | Probability, decision and information theories in machine learning and pattern recognition. | ||
| 5. | Probability distributions. Mixture models and EM. | ||
| 6. | Linear models for regression and classification. | Regularisation. | |
| 7. | Algorithm-independent machine learning. Evaluating hypothesis. No free lunch theorem, Occam's razor and cross-validation. High-dimensional problems. Feature selection. | Principal component analysis. Feature extraction. Naive Bayes Classifier. Comparing methods. |
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 | No | 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 | No | Suomi |
Prerequisites
| Course | Mandatory/Advisable | Description |
| SGN-13000 Introduction to Pattern Recognition and Machine Learning | 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 |
| Postgraduate 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 research and development work. The practical exercises (Matlab) are essential part of the course giving the possibility to utilize the methods in practical problems. |