IFT 4030/7030: Machine Learning for Signal Processing (MLSP)

Instructor : Cem Subakan

This class is about learning to build machine learning algorithms for signals. Different from a standard machine learning class, we will have a little bit more of an EE flavor to things. That is, we will often work with sequential data such as speech and audio, and other signals. We will give the necessary background to be able to propose and carry out a research or applied project in the domain of machine learning for signal processing. In the end, our goal is to teach how to fish (for MLSP projects)!

This class is influenced by classes with the same title in UIUC, CMU, and Indiana University.

Schedule (Tentative)

Week 1 : Linear Algebra Refresher slides, lab

Week 2 : Probability Refresher slides, lab

Week 3: Signal Processing Refresher slides, lab

Week 4: Machine Learning 1: Decompositions slides, lab

Week 5: Machine Learning 2: Non-linear Dimensionality Reduction slides, lab

Week 6: Machine Learning 3: Classification slides, lab

Week 7: Deep Learning Primer slides, lab

Week 8 (Invited Lecture by Sara Karami) slides

Week 9: Machine Learning 4: Clustering slides, lab

Week 10: Time Series Models slides, lab

Week 11: Graph Signal Processing / Graph ML slides, lab

Week 12: Speech / Audio slides

Week 13: Project Presentations


There will be 3 homeworks (45%), labs (10%) and a project (45%) that will be carried out by teams of 2-3 students. It is preferable that the students propose the project, but we will propose several projects ideas also.