SGN-13006 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: SGN-13006 2015-01

Study type P1 P2 P3 P4 Summer

Lectures
Excercises
28 h/per
12 h/per

Lecture times and places: Tuesday 11 - 12 TB223 , Wednesday 12 - 14 S1/SA201 , Tuesday 10 - 12 S2/SA203 , Wednesday 12 - 14 S2/SA203 , Tuesday 10 - 12 TB111

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. 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 (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 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 it is assumed that the student has good knowledge of 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

Last modified 19.03.2015

Study type	P1	P2	P3	P4	Summer
Lectures Excercises	28 h/per 12 h/per

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.

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

Course	Corresponds course	Description
SGN-13006 Introduction to Pattern Recognition and Machine Learning, 5 cr	SGN-2506 Introduction to Pattern Recognition, 4 cr