Signal Processing and Machine Learning, 30 op
Opintokokonaisuuden tyyppi
Advanced Studies
Yhteyshenkilö
Sari Peltonen, Joni Kämäräinen, Heikki Huttunen
Osaamistavoitteet
| - | 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. |
Esitietovaatimukset
| Opintokokonaisuus | Opintopisteet | P/S | Lisätiedot |
| 30 op | Mandatory | If the student finishes a B.Sc. degree elsewhere than Computing and Electrical Engineering at TUT, then the prerequisite courses are determined by those of the individual courses of this study module. |
Sisältö
Pakolliset opintojaksot
In addition to the two compulsory courses (10 credits), the student must select one of the three Selective Sub-modules: 1) Imaging and Computer Vision 2) Artificial Intelligence, or 3) Audio and Signal Processing (required courses for each are listed below).
| Opintojakso | Opintopisteet | Vuosikurssi |
| SGN-21006 Advanced Signal Processing | 5 op | IV |
| SGN-41007 Pattern Recognition and Machine Learning | 5 op | IV |
| Yhteensä | 10 op |
Pakolliset vaihtoehtoiset opintojaksot
Optional compulsory courses are divided under the three selective sub-modules (20cr each): 1) Imaging and Computer Vision, 2) Artificial Intelligence and 3) Audio and Signal Processing. Student must select one of the sub-modules, but we also support other creative combinations (in that case contact the responsible teachers of the module). Students are encouraged to also propose other optional courses (contact the module responsible teachers)
Must be selected at least 20 credits of courses
| Opintojakso | Opintopisteet | Vaihtoehtoisuus | Vuosikurssi |
| SGN-22006 Signal Compression | 5 op | 3 | IV |
| SGN-24007 Advanced Audio Processing | 5 op | 3 | IV |
| SGN-25006 Vector Space Methods for Signal and Image Processing | 5 op | 1, 3 | IV |
| SGN-26006 Advanced Signal Processing Laboratory | 5 op | 1, 2, 3 | IV |
| SGN-31007 Advanced Image Processing | 5 op | 1 | IV |
| SGN-33007 Media Analysis | 5 op | 1, 2, 3 | IV |
| SGN-34006 3D and Virtual Reality | 5 op | 1 | IV |
| SGN-43006 Knowledge Mining and Big Data | 5 op | 2, 3 | IV |
| SGN-44006 Artificial Intelligence | 5 op | 2 | IV |
| SGN-81006 Signal Processing Innovation Project | 5-8 op | 1, 2, 3 | IV |
| TIE-22307 Data-Intensive Programming | 5 op | 2 | IV |
1.
Valittava 20 opintopistettä.
Selective Module 1: Imaging and Computer Vision. Only one of the laboratory courses (SGN-26006, SGN-81006) can be included.
2.
Valittava 20 opintopistettä.
Selective Module 2: Artificial Intelligence and Machine Learning.
Course SGN-44006 Artificial Intelligence is compulsory for students reading this selective module. Only one of the laboratory courses (SGN-26006, SGN-81006) can be included.
3.
Valittava 20 opintopistettä.
Selective Module 3: Audio and Signal Processing.
Course SGN-24007 Advanced Audio Processing is compulsory for students reading this selective module. Only one of the laboratory courses (SGN-26006, SGN-81006) can be included.
Täydentävät opintojaksot
Additionally, the student may propose alternative courses to substitute items from the above list.
| Opintojakso | Opintopisteet |
| SGN-90666 Signal Processing Colloquium | 1-5 op |
Lisätiedot
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.