Course Catalog 2014-2015
Basic

Basic Pori International Postgraduate Open University Search

|Degrees|     |Study blocks|     |Courses|    

Course Catalog 2014-2015

SGN-13006 Introduction to Pattern Recognition and Machine Learning, 5 cr

Additional information

Lectures and exercises in English.

Person responsible

Joni Kämäräinen

Lessons

Study type P1 P2 P3 P4 Summer Implementations Lecture times and places
Lectures
Excercises
 28 h/per
 12 h/per


 


 


 


 
SGN-13006 2014-01 Tuesday 11 - 12 , TB223
Wednesday 12 - 14 , TB222
Tuesday 10 - 12 , S2
Wednesday 12 - 14 , S2
Tuesday 10 - 12 , SE201
Lectures
Excercises


 


 


 


 
 36 h/per
 18 h/per
SGN-13006 2014-02 Monday 9 - 12 , TB222
Tuesday 9 - 12 , TB224
Wednesday 9 - 12 , TB222
Tuesday 9 - 12 , TB222

Requirements

Final examination and exercises.
Completion parts must belong to the same implementation

Learning Outcomes

The student understands the main concepts and fundamental approaches in pattern recognition and machine learning. Most 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 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. Instance-based learning.     
9. Pattern recognition and machine learning in robotics and re-inforcement learning.     

Instructions for students on how to achieve the learning outcomes

In order to pass the course the student has to pass the exam and make the exercises.

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, 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    English  
Book   Machine Learning   Tom Mitchell   0070428077     Contents of many lectures follow this book   Yes    English  
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    English  

Additional information about prerequisites
No mandatory requirements, but it is assumed that a student has good knowledge of BSc level engineering mathematics and programming.

Prerequisite relations (Requires logging in to POP)



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  

More precise information per implementation

Implementation Description Methods of instruction Implementation
SGN-13006 2014-01 This course provides basic understanding of pattern recognition and machine learning methods needed for signal and data interpretation.        
SGN-13006 2014-02 Summer implementation of SGN-13006. Course contents and exam requirements are same as in the implementation of Period 1 of 2014. However, certain practicalities differ. Detailed operational information is given in the slides of the first lecture.        

Last modified01.04.2015