Course Catalog 2014-2015
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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
Lectures
Excercises


 


 
 4 h/week
 2 h/week


 


 
SGN-41006 2014-01 Tuesday 10 - 12 , TB222
Thursday 10 - 12 , TB223

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:

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       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 
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 2014-01 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.        

Last modified15.01.2015