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

Additional information

Lectures are in English, but exercise sessions are given in both Finnish and English.

Person responsible

Joni Kämäräinen


Implementation Period Person responsible Requirements
SGN-13006 2019-01 1 Joni Kämäräinen
Active participation, homeworks, exercises and exam.

Learning Outcomes

This course will provide a broad introduction to Pattern Recognition (PR) and Machine Learning (ML). The course is programming oriented concentrating on models of learning (data structures to establish these models) and methods of learning (algorithms to modify data structures according to training data). After the course students will know the main approaches to machine learning starting from early ideas to the most recent ones. Students will also obtain skills to implement ML&PR methods and evaluate them with real data.


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. Multi-layer perception neural networks and support vector machines.     
7. Unsupervised learning including clustering, self-organising map and linear methods (principal component analysis)     
8. Pattern recognition and machine learning in robotics and re-inforcement 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 (0-5)

Partial passing:

Completion parts must belong to the same implementation

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   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   

Additional information about prerequisites
No mandatory requirements, but students are assumed to have good knowledge about BSc level engineering mathematics and programming.

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  
SGN-13006 Introduction to Pattern Recognition and Machine Learning, 5 cr SGN-13000 Introduction to Pattern Recognition and Machine Learning, 5 cr  

Updated by: Kunnari Jaana, 05.03.2019