Advanced Studies in Signal Processing and Machine Learning, At least 80 cr

The major in Signal Processing and Machine Learning is designed for active and diligent students who are interested in the latest and still-developing technologies in information technology. At present, such topics include, for example, image, language, and sound processing, computer vision, machine learning, and robotics.

The module consists of six mandatory courses (30 cr), a set of free-choice studies (20 cr), and a master's thesis (30 cr). In terms of free-choice studies, students can create their own course package from the offered options or alternatively choose from the listed thematic specializations below.

Please note the possible prerequisites (order of course completion) when planning your studies.

Thematic specializations consist of the following subject areas and courses:

Imaging

COMP.SGN.230 Vector Space Methods for Signal and Image Processing
COMP.SGN.300 Advanced Image Processing
COMP.SGN.320 3D and Virtual Reality
COMP.SGN.330 Computational Imaging and Digital Holography
COMP.SGN.350 Imaging Sensors and Systems

Machine hearing and vision

COMP.SGN.220 Advanced Audio Processing
COMP.SGN.340 Speech Processing
COMP.SGN.300 Advanced Image Processing
DATA.ML.300 Computer Vision

Signal processing and data analysis

COMP.SGN.230 Vector Space Methods for Signal and Image Processing
COMP.SGN.240 Advanced Signal Processing Laboratory
COMP.SGN.300 Advanced Image Processing
DATA.ML.330 Media Analysis
DATA.ML.390 Computational Diagnostics of Data
DATA.ML.430 Complex Networks

The module provides students with strong theoretical knowledge and practical skills in all the subject areas of our courses. Students are also welcome to become members of research groups during their studies.

• The student knows how to adopt and adapt signal processing and machine learning methods for real problems.

• The student is able to find state-of-the-art methods and adapt them to to solve practical problems.

• The student knows the tools (Matlab) and software libraries (Python) for machine learning and signal processing problems.

• The student knows how to apply machine learning and signal processing methods in audio, speech, language, imaging, vision and robotic applications.