Signal Processing and Machine Learning, 30 cr

Type of the study module

Intermediate Studies

Contact

Sari Peltonen, Heikki Huttunen, Joni Kämäräinen

Learning Outcomes

- Opiskelija osaa soveltaa koneoppimis- ja signaalinkäsittelymenetelmiä tietotekniikassa.
Student knows how to adopt and adapt signal processing and machine learning methods for real problems.

Opiskelija kykenee löytämään kehittyneitä menetelmiä kirjallisuudesta sekä muokkaamaan niitä käsillä olevaan ongelmaan sopivaksi.
Student is able to find state-of-the-art methods and adapt them to to solve practical problems.

Opiskelija osaa käyttää Matlabia/Python-kirjastoja koneoppimis- ja signaalinkäsittelyongelman laskennalliseen ratkaisemiseen.
The student can use Matlab/Python libraries to solve machine learning and signal processing problems.

Opiskelija osaa soveltaa koneoppimista ja älykkäitä menetelmiä audion, kuvan ja robotiikan sovellusalueilla.
The student can apply pattern recognition, machine learning and signal processing method in audio, vision and robotics.

Prerequisites

Opintojakso SGN-11000 Signaalinkäsittelyn perusteet on pakollinen esitieto. Jos opintojakso SGN-11000 Signaalinkäsittelyn perusteet ei kuulu opiskelijan pakollisiin perusopintoihin, sisällytetään se tähän moduuliin. Tällöin opiskelija voi halutessaan suorittaa vain toisen opintojaksoista SGN-14007 Introduction to Audio Processing ja SGN-12001 Johdatus kuvan- ja videonkäsittelyyn. The course SGN-11007 Introduction to Signal Processing is a mandatory prerequisite. If the course SGN-11007 Introduction to signal processing is not part of mandatory basic studies, the student may include it into this module. In this case, it is allowed to take only one of the courses SGN-14007 Introduction to Audio Processing ja SGN-12007 Introduction to Image and Video Processing. Courses SGN-11000 and SGN-11007 share the same contents and student should take only one of them. ( Mandatory )
Passing the module requires programming skills and understanding of the basic engineering mathematics. ( Advisable )

Further Opportunities

Study block Credit points
Robotics 30 cr
Signal Processing and Machine Learning 30 cr

Content

Compulsory courses

Course Credit points Additional information Class
SGN-12001 Johdatus kuvan- ja videonkäsittelyyn 5 cr 1 III  
SGN-12007 Introduction to Image and Video Processing 5 cr 1 III  
SGN-13006 Introduction to Pattern Recognition and Machine Learning 5 cr III  
SGN-14007 Introduction to Audio Processing 5 cr III  
SGN-16006 Bachelor's Laboratory Course in Signal Processing 5 cr III  
Total 25 cr    

1. Select only one of the two alternative courses

Optional Compulsory Courses

SGN-80000 Signaalinkäsittelyn kandidaattiseminaari on pakollinen, mikäli kokonaisuuteen tehdään kandidaatintyö. SGN-80000 is compulsory to students who do this module as major in their B.Sc. degree.

Course Credit points Class
SGN-80000 Signaalinkäsittelyn kandidaattiseminaari 0 cr III  

Complementary Courses

Kokonaisuus voidaan suorittaa sivuaineena suorittamalla moduulin 4 pakollista kurssia, jolloin laajuudeksi tulee 20 op. Pääaineena laajuus on 30 op. Note that for several of the courses there are Finnish/English alternatives in which case either (but only one) is accepted. Note also that the list is not exhaustive and students may propose suitable alternative courses to substitute those in the list (discuss with the major responsible persons). The students of the international BSc programme can study this module in 20 credits volume and do not need to study any of these additional courses.

Should be completed to the minimum study module extent of 30 ETCS

Course Credit points Class
ASE-9306 Introduction to Robotics and Automation 5 cr III  
MAT-02450 Fourier'n menetelmät 4 cr III  
MAT-02550 Tilastomatematiikka 4 cr II  
MAT-60456 Optimization Methods 5 cr III  
MAT-61006 Introduction to Functional Analysis 7 cr III  
MAT-64750 Mallinnus ja optimointi 4 cr III  
TIE-20106 Data Structures and Algorithms 5 cr III  
TIE-22307 Data-Intensive Programming 5 cr III  

Additional information

Signal processing and machine learning are central fields in the modern information technology and will play a predominant role in digitalization of the society. Signal processing and machine learning applications are vast varying from computer vision systems, audio, speech and signal processing applications to robotics and human-robot interaction. Another related field is data engineering which means signal processing, machine learning and pattern recognition algorithms applied to large datasets from finance, security and safety, health and biotechnology, Internet content and so on. This module provides students strong practical knowledge and expertise on the main approaches and methodologies of signal processing, machine learning and pattern recognition. Moreover, the students will have hands-on experience on the most emerging applications of these: computer vision, audio and speech processing and data engineering.

It is also possible to take the module as a minor with 20 credits worth of courses (the mandatory ones in the list).

Updated by: Torikka Mari, 13.03.2019