Course Information

Course Code:
Course Number:
Code Course Name Language Type
EHB 328E Machine Learning for Signal Processing English Elective
Local Credits ECTS Theoretical Tutorial Laboratory
3 6 3 0 0
Course Prerequisites and Class Restriction
Prerequisites (MAT 271 MIN DD
or MAT 271E MIN DD
or EEF 271 MIN DD
or EEF 271E MIN DD)

and (MAT 281 MIN DD
or MAT 281E MIN DD
or EEF 281 MIN DD
or EEF 281E MIN DD)

Class Restriction None
Course Description
The course will include the following topics: Data-driven representations. Principal Component Analysis (PCA) and Kernel PCA. Independent Component Analysis (ICA). Non-negative matrix factorization (NMF). Dictionary based, sparse and overcomplete data representations. Low rank matrix representations. Regression and Linear prediction. Stochastic Gradient Descent and LMS adaptive filters. Clustering and Classification. Neural Networks. Convolutional networks and applications to signal and image processing. A good knowledge of probability theory, linear algebra and signals and systems theory is a prerequisite for the course. The term project and homework will necessitate software simulations.