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Study module, curriculum year 2020–2021
TTY-SY_TST-1

Signal Processing and Machine Learning, At least 30 cr

Tampere University
Description
This MSc (Tech) major module strengthens students' knowledge on modern signal processing and machine learning. Module will specifically focus on state-of-the-art technologies and approaches that are requested in the top notch companies of data engineering, autonomous cars, robotics and so on. Under this major module there are three optional sub-modules which provide in-depth and state-of-the-art knowledge in one of the three most emerging fields: 1) vision and imaging, 2) audio and signal processing and 3) artificial intelligence. The major will thus consist of 10 cr of common courses and at least 20 cr of courses in one of the three sub-modules. This way we provide strong knowledge on signal processing and machine learning and one additional special area where the skills become expert level. All three modules provide strong knowledge and skills appreciated by the companies and especially their R&D units. Moreover, the students are encouraged to add robotics courses to their studies.
Objectives
  • Student is able to study state-of-the-art in machine learning and signal processing methods and can adopt and adapt these techniques.

    Student is able to design, train and deploy a machine learning model for classification and regression. She can also design a system for processing data from the domains of one of the three sub-modules.
Study module code
TTY-SY_TST-1
Language of instruction
English
Academic year
2020–2021
Level of study
Advanced studies
Fields of study
Engineering, Manufacturing and Construction
Persons responsible
Responsible teacher:
Joni Kämäräinen
Responsible teacher:
Sari Peltonen
Responsible teacher:
Heikki Huttunen
Prerequisites
Studies that include this module
Study module code
TTY-SY_TST-1
Language of instruction
English
Academic year
2020–2021
Level of study
Advanced studies
Fields of study
Engineering, Manufacturing and Construction
Persons responsible
Responsible teacher:
Joni Kämäräinen
Responsible teacher:
Sari Peltonen
Responsible teacher:
Heikki Huttunen