DS-GA 1013 / MATH-GA 2821 Optimization-based Data AnalysisInstructor: Carlos Fernandez-Granda (cfgranda@cims.nyu.edu) This course provides a unifying description of optimization-based methods designed to tackle data-analysis problems, including sparse regression, compressed sensing, super-resolution, matrix completion, clustering and manifold learning. We will analyze these techniques from a mathematical and algorithmic point of view and describe their application to a wide range of practical problems. See the schedule for more details. Announcements
General InformationPrerequisitesCalculus, linear algebra and probability (at the level of the DS GA 1002 notes). Some programming skills and some exposure to statistics, machine learning or optimization are desirable. LectureWednesday 3:30-5:10 pm, 60 5th Ave (CDS) room 110 RecitationMonday 5:20-6:10 pm, 60 5th Ave (CDS) room 110 Office hoursCarlos: Friday 5:30-6:30 pm, 60 5th Ave (CDS) room 606 Grading policyBiweekly homework (50%) + Project proposal (10%) + Project (40%) HomeworkHomework deadlines are posted on the schedule. The assignments should be submitted as a pdf through NYU classes. Any code you write should be submitted in a zip file. The solutions and the grades will be available also on NYU classes. Feel free to discuss the homework with other students in person or on Piazza, but do not share specific answers and make sure that you write your own personal solutions yourself. Always explain your thought process. If you use results from the notes or a book reference them adequately. BooksWe will provide self-contained notes. Some useful additional references are
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