
Course Catalog 20142015
SGN13006 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 
















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.  Multilayer perception neural networks and support vector machines.  
7.  Unsupervised learning including clustering, selforganising map and linear methods (principal component analysis)  
8.  Instancebased learning.  
9.  Pattern recognition and machine learning in robotics and reinforcement 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 (15) 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, 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  9780470682272  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 


More precise information per implementation
Implementation  Description  Methods of instruction  Implementation 
This course provides basic understanding of pattern recognition and machine learning methods needed for signal and data interpretation.  
Summer implementation of SGN13006. 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. 