Machine Learning, 25 cr
Type of the study module
Intermediate Studies
Contact
Heikki Huttunen, Ari Visa, Joni Kämäräinen
Learning Outcomes
| - | After having passed the module the student can implement software systems adopting and adapting modern machine learning and pattern recognition methods. |
Prerequisites
Passing the module requires programming skills and understanding of the basic engineering mathematics. ( Advisable )
Content
Compulsory courses
| Course | Credit points | Additional information | Class |
| SGN-11000 Signaalinkäsittelyn perusteet | 5 cr | 1 | II |
| SGN-13000 Johdatus hahmontunnistukseen ja koneoppimiseen | 5 cr | 2 | III |
| SGN-41007 Pattern Recognition and Machine Learning | 5 cr | III | |
| Total | 15 cr |
1. Kurssi vaihtoehtoinen kurssin SGN-11006 kanssa.
Student may select either SGN-11000 (Finnish) or SGN-11006 (English).
2. Kurssi vaihtoehtoinen kurssin SGN-13006 kanssa.
Student may select either SGN-13000 (Finnish) or SGN-13006 (English).
Optional Compulsory Courses
SGN-80000 Signaalinkäsittelyn kandidaattiseminaari on pakollinen, mikäli kokonaisuuteen tehdään kandidaatintyö. SGN-80000 is compulsory only to those students who have Signal processing as their major in their B.Sc. degree.
Complementary Courses
Students of international BSc program take at least 5 cr from the list.
Should be completed to the minimum study module extent of 25 ETCS
| Course | Credit points | Additional information | Class |
| ASE-2916 Robotics and Automation | 5 cr | III | |
| ASE-7410 Kuvaan perustuvat mittaukset | 5 cr | III | |
| MAT-02550 Tilastomatematiikka | 4 cr | II | |
| MEI-56606 Machine Vision | 5 cr | III | |
| SGN-12000 Kuvan- ja videonkäsittelyn perusteet | 5 cr | 1 | III |
| SGN-14006 Audio and Speech Processing | 5 cr | III | |
| SGN-84007 Introduction to Matlab | 1 cr | II | |
| TIE-20100 Tietorakenteet ja algoritmit | 5 cr | 2 | II |
1. The course SGN-12006 in English is an alternative to SGN-12000.
2. The course TIE-20106 in English is an alternative to TIE-20100.
Additional information
Machine learning is the central concept in the modern information technology and will play a predominant role in digitalization of the society. Its applications are vast varying from computer vision systems, audio and speech processing applications to robotics and human-robot interaction. Another emerging field is big data which means that machine learning algorithms and pattern recognition are applied to large datasets from finance, security and safety, health and biotechnology, Internet content etc. This module provides the students strong practical knowledge and expertise on the main approaches and methodologies of machine learning and pattern recognition. Moreover, the students will have hands-on experience on the most emerging applications of machine learning: computer vision, audio and speech processing and big data.