| 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
or EEF 206 MIN DD
or EEF 206E 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. |
|
