IFT 6760A - Winter 2020
Matrix and tensor factorization techniques for machine learning



[new] The page of last year's edition of the class is available here for references.

Description

The goal of this course is to present an overview of linear and multilinear algebra techniques for designing/analyzing ML algorithms and models, and to engage students with new research in the area.
- Fundamental notions of linear and multilinear algebra.
- Old and new ML methods leveraging matrix and tensor decomposition: PCA/CCA, collaborative filtering, spectral graph clustering, spectral methods for HMM, K-FAC, spectral normalization, tensor method of moments, NN/MRF compression, tensor regression/completion, etc.
- Open problems.

Class Info

When: Tuesdays 12:30-2:30PM and Thursdays 11:30-1:30PM
Where: Mila (room TBD), 6650 St-Urbain street
Instructor: Guillaume Rabusseau, Office: 3151.André-Aisenstadt @ UdeM / D.15 @ Mila.
Office hours: Tuesdays after class

Evaluation

Class project (40%) + Assignments (20%) + Paper presentation (30%) + Scribing (10%)

The latex template for scribing can be found in this overleaf project.

Tentative list of topics (to be updated as we go along)

Resources

Linear Algebra

Tensor Decomposition