**Fall 2015: EE 520: Special
Topics: ****Statistical Machine Learning**

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

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

Time : Tues-Thurs 9:30-10:50

Location: Howe 1220

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 new algorithms and guarantees for alternating minimization based solutions for various problems: low-rank matrix completion, phase retrieval, etc.

- Recent work on rankings` and individualized rankings` estimation (collaborative ranking)

- Key papers on batch and online methods for low-rank matrix completion, robust PCA and sparse recovery and guarantees for the proposed methods

- Applications in recommendation systems` design / Netflix problem and in computational imaging

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.

**Class time and Location:****Tues-Thurs 9:30-10:50**in**Howe 1220****Instructor:**Prof. Namrata Vaswani**Office Hours:**Tues 2-3, Wed 10-11 or by appointment**Email:**namrata AT iastate.edu,**Phone:**515-294-4012**Office:**3121 Coover Hall**Grading:**- 10% class participation
- 40% scribe notes for
one paper that I will present
- 50% term paper (read
and present on a paper/topic for one lecture, submit slides and a short
report)
**Prerequisites/Corequisites:****EE 523 (or at least EE 322 level knowledge), Math 510 (linear algebra), EE 571 (convex optimization) or at least one of these. I strongly recommend taking Math 510 (also offered in Fall) and going over EE 322 notes (given below)****Disability accommodation:**If you have a documented disability and anticipate needing accommodations in this course, please make arrangements to meet with me soon. You will need to provide documentation of your disability to Disability Resources (DR) office, located on the main floor of the Student Services Building, Room 1076, 515-294-7220.**Papers that can be presented by students: TBD**

**Tentative
List of Papers (some of these can be used by students for their term paper;
more paper suggestions are welcome)**

**Introduction slides:**Intro**Background material: probability, linear algebra, and optimization****Probability:**Quick recap of EE322 (undergraduate probability for EE), law of large numbers, high probability tail bounds for random matrix eigenvalues- EE
322 notes
- EE
322 problem sets (many of these are harder than what I use for my
EE 322 offerings)
- Notes
**Linear Algebra:**parts of Chapters 0, 1, 2, 4, 5 of Matrix Analysis, Horn and Johnson**Optimization:**subset of slides of Vandenberghe and Boyd:**Papers****Advanced Topics in Probability and Linear Algebra:**- Vershynin`s review
article: Introduction
to the non-asymptotic analysis of random matrices
- Key results from
Tropp`s paper: User-friendly
Tail Bounds for Sums of Random Matrices
**Compressed Sensing:**- Decoding
by linear programming (Candes and Tao)
- The
Restricted Isometry Property and Its Implications for Compressed Sensing
(Candes)
**Matrix Completion and Robust PCA**- Exact
Matrix Completion via Convex Optimization (Recht and Candes)
- Robust
Principal Component Analysis?
(Candes, Li, Wright, Ma)
**Alternating Minimization (AltMin) solutions for Non-Convex Problems (with appropriate initialization)**- Low-rank Matrix
Completion using Alternating Minimization (Jain, Netrapalli, Sanghavi)
- Statistical
guarantees for the EM algorithm: From population to sample-based
analysis
- Phase retrieval via
Alternating Minimization
(Jain, Netrapalli, Sanghavi)
**Gradient Descent type solutions for Non-Convex Problems (with appropriate initialization)**- Phase retrieval via
Wirtinger Flow: Theory and Algorithms
(Soltankhatabi and Candes)
- Solving Random
Quadratic Systems of Equations is Nearly as Easy as Solving Linear
Equations (Chen and Candes)
- Others
**Collaborative Ranking: Rankings and individualized rankings` estimation**- Individualized Rank
Aggregation using Nuclear Norm Regularization
- Others - TBD

Older EE520 on Matrix
Completion and Robust PCA: here, Even
older EE520 on Compressive Sensing: here