Fall 2016: EE 520: Special Topics: Topics in Statistical Machine Learning

Instructor: Prof Namrata Vaswani email: namrata@iastate.edu

Web link: http://home.engineering.iastate.edu/~namrata/MachineLearning_class/

Time : Mon-Wed 11-12:20

Location: Coover 2222 (until further notice)

 

This will be a special topics / seminars course in which we will discuss some recent works on statistical machine learning algorithms, performance guarantees and applications. One part of the class will be taught by me, the second one will involve term paper presentations by the students. Some topics that will be covered include

-          Review of background needed

o   Probability

o   Linear Algebra

o   Convex optimization

-          Advanced topics on Probability and Linear Algebra (used in papers that we will discuss)

-          Recent work on non convex methods (e.g. Alternating Minimization or Gradient Descent) for various problems: low-rank matrix completion, phase retrieval, etc.

-          Low-rank matrix completion, robust PCA and sparse recovery and guarantees for the proposed methods

-          Modern applications

This course will be of interest to graduate students from Electrical and Comp Engineering, Mathematics, Statistics, Computer Science, Industrial Engineering and others. Topics can be added based on the students’ research interests.


List of Papers

Term papers and Scribing – New 9/14/2016

Term paper topics: PCA etc.

- Review of PCA literature

- Incremental (or online or streaming) PCA

      - Memory Limited, Streaming PCA, NIPS 2013

      - Online Principal Components Analysis

- Non-convex Robust PCA, NIPS 2014

- Work on partial SVD (top k singular vectors)

      - Musco and Musco NIPS 2015, Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition

      - Back-references of this work

 

Scribe topics

1. Add to probability and linear algebra notes 2. Find linear algebra tricks in all the papers we present 3. Find probability tricks in all the papers we present 4.

Phase retrieval via Alternating Minimization (Jain, Netrapalli, Sanghavi) Solving Random Quadratic Systems of Equations is Nearly as Easy as Solving Linear Equations (Chen and Candes) Low-rank Matrix Completion using Alternating Minimization (Jain, Netrapalli, Sanghavi)

Tentatively: R. Vershynin, ESTIMATION IN HIGH DIMENSIONS: A GEOMETRIC PERSPECTIVE

 

 

 

A Partial List of Papers for students to pick from