SGN13006 Introduction to Pattern Recognition and Machine Learning, 5 cr
Additional information
Lectures and exercises in English.
Person responsible
Jari Niemi, Joni Kämäräinen
Lessons
Implementation 1: SGN13006 201501
Study type  P1  P2  P3  P4  Summer 






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 the principal idea of 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.  Pattern recognition and machine learning in robotics and reinforcement learning. 
Instructions for students on how to achieve the learning outcomes
You must actively participate the lectures and do the exercises. In particular, familiarize yourself with the exercise questions before the exercise session.
Assessment scale:
Numerical evaluation scale (15) will be used on the course
Partial passing:
Study material
Type  Name  Author  ISBN  URL  Additional information  Examination material 
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  
Book  Machine Learning  Tom Mitchell  0070428077  Contents of many lectures follow this book  Yes  
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 
Additional information about prerequisites
No mandatory requirements, but it is assumed that the student has good knowledge of BSc level engineering mathematics and programming.
Correspondence of content
Course  Corresponds course  Description 
SGN13006 Introduction to Pattern Recognition and Machine Learning, 5 cr  SGN2506 Introduction to Pattern Recognition, 4 cr 