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MATH-GA 2830.002 Advanced Topics in Applied Math: MATHEMATICS OF DATA SCIENCE
(Fall 2016, NYU Courant Math; aka: CSCI-GA 2930)

Afonso S. Bandeira
bandeira [at] cims [dot] nyu [dot] edu

Lectures: Wed 11am-12.50pm at CIWW1302 (Note the room change).

Office Hours: Tue 10.30am-12.30pm at CIWW1123 (or by appointment)
I also hold Office Hours for another course Wed 2.30pm-4.30pm (at CDS in 60 5th Av., Room 603).

Piazza page available here.

  • Homework 2 is now available (it is optional)
  • IMPORTANT DATES: Nov16: abstracts due, Dec 7: project report due and presentations. See more detail in syllabus.
  • Lecture notes now available, will be updated continuously 
  • NEW: Please sign up to the piazza page here for announcements
  • Room for the lectures has changed to CIWW1302.
  • This course will be similar to a course I gave, which is available at MIT OCW here.
  • As the semester progresses I will post Lecture Notes and update the Open Problem list. For last years version of the notes and open problems, see: Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science.

Working knowledge of linear algebra and basic probability is required. Some familiarity with the basics of optimization and algorithms is also recommended.

Syllabus: A mostly self-contained research-oriented and fast-paced course designed for PhD students with an interest in doing research in theoretical aspects of algorithms that aim to extract information from data. These often lie in overlaps of (Applied) Mathematics with: Computer Science, Electrical Engineering, Statistics, and/or Operations Research. Each lecture will feature a couple of Mathematical Open Problem(s) with relevance in Data Science. The main mathematical tools used will be Probability and Linear Algebra, and a basic familiarity with these subjects is required. There will also be some (although knowledge of these tools is not assumed) Graph Theory, Representation Theory, Applied Harmonic Analysis, among others. The topics treated will include Dimension reduction, Manifold learning, Sparse recovery, Random Matrices, Approximation Algorithms, Community detection in graphs, and several others. Take a look at the Syllabus for a more detailed list of the topics covered.

Open problems will be presented at the end of most lectures.
In fact, the Syllabus already contains two such problems. Some of the open problems will also be posted in my blog.
I am here to help: please let me know of your goals for your project and keep me up to date of your progress on it. If you have any question, want to discuss a problem, or brainstorm about any research idea, just email me and we'll schedule a time to meet.
Feedback: Also, if you have any comment or feedback on the class (it's going too fast, too slow, you want me to cover more of something, or less of something else, etc) please let me know (in person or through email) or submit a comment to this google form. Having direct feedback from you is the best way for me to try give lectures that you like! (keep in mind that I don't know who sent me the comment or feedback and there is no way for me to answer, for questions use email, or my blog).

Lecture Notes: 
  • September 7, 2016: 0: Syllabus and two open problems
  • September 14, 2016: Sections 1.1. and 1.2. of Lecture Notes available here.
  • September 21, 2016: Section 1.3 of Lecture Notes available here.
  • September 28, 2016: Section 2.2. of Lecture Notes available here.
  • October 5, 2016: Section 2.3 of Lecture Notes available here.
  • October 12, 2016: First half of Section 3 of Lecture Notes available here.
  • Joan Bruna's guest lecture: slides available here.
  • October 26, 2016: Second half of Section 3 of Lecture Notes available here.

Homework (optional):

Open Problems: