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18.S096:
Topics in Mathematics of Data Science
(Fall 2015, MIT Mathematics) Afonso S. Bandeira bandeira [at] cims [dot] nyu [dot] edu Lectures: TR 1112.30pm at E17122 Afonso's Office Hours: T 24pm (or by appointment) TA: Menglu Wang. Menglu's Office hours: Wed 2.304pm at E18301W Announcements:
Prerequisites: Working knowledge of linear algebra and basic probability is required. Some familiarity with the basics of optimization and algorithms is also recommended. Syllabus: This will be a mostly selfcontained researchoriented course designed for undergraduate students (but also extremely welcoming to graduate 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 two or more of the following: Mathematics, Applied Mathematics, Computer Science, Electrical Engineering, Statistics, and/or Operations Research. 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. 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: See all (updated) Lecture Notes here
Open Problems (with links to descriptions on my blog):

