Course Catalog 2013-2014
Postgraduate

Basic Pori International Postgraduate Open University

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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
Lectures
Excercises


 


 
 4 h/week
 2 h/week


 


 
SGN-41006 2013-01 Tuesday 10 - 12, TB222
Thursday 10 - 12, TB223

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:

Completion parts must belong to the same implementation

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 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 
SGN-41006 Signal Interpretation Methods, 4 cr SGN-2556 Pattern Recognition, 5 cr  

More precise information per implementation

Implementation Description Methods of instruction Implementation
SGN-41006 2013-01 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.        

Last modified30.12.2013