View source for 10-601 Matrix Factorization

This a lecture used in the [[Syllabus for Machine Learning 10-601 in Fall 2014]]

=== Slides ===

* [http://www.cs.cmu.edu/~wcohen/10-601/pca+mf.ppt Slides in PowerPoint], [http://www.cs.cmu.edu/~wcohen/10-601/pca+mf.pdf  in PDF].

=== Readings ===

* PCA is not covered in Mitchell.  Bishop chapter 12 is optional reading.
* There are also some [http://www.cs.cmu.edu/~wcohen/10-601/PCA-notes/pca.pdf notes on PCA/SVD] that I've written up.
* There's a nice description of [http://people.mpi-inf.mpg.de/~rgemulla/publications/rj10481rev.pdf the gradient-based approach to MF], and a scheme for parallelizing it,by Gemulla et al.

===  Summary  ===

You should know:
* What PCA is, and how it relates to matrix factorization.
* What loss function and constraints are associated with PCA - i.e., what the "PCA Problem" is.
* How to interpret the low-dimensional embedding of instances, and the "prototypes" produced by PCA and MF techniques.
** How to interpret the prototypes in the case of dimension reduction for images.
** How to interpret the prototypes in the case of collaborative filtering, and completion of a ratings matrix.
* How PCA and MF relate to k-means and and EM.

* The differences/similarities between PCA and SVD.
* The connection between SVD and LSI.