DSGA 1013 / MATHGA 2821 Mathematical Tools for Data ScienceInstructor: Carlos FernandezGranda (cfgranda@cims.nyu.edu) This course provides a rigorous introduction to mathematical tools for data science drawn from linear algebra, harmonic analysis, probability theory, and convex analysis. The main topics are the singularvalue decomposition (SVD), the Fourier series, randomized projections, the randomized SVD, convex optimization, duality theory and nonconvex optimization. The material is motivated by multiple dataanalysis applications including dimensionality reduction, collaborative filtering, sound and image processing, magneticresonance imaging, sparse regression, compressed sensing, and topic modeling. See the schedule for more details. Announcements
General InformationPrerequisitesCalculus, linear algebra (at the level of DSGA 3001) and probability (at the level of the DS GA 1002 notes) are essential prerequisites. Some programming skills and some exposure to statistics, machine learning or optimization are desirable. LectureThursday 3:305:10 pm, 60 5th Ave (CDS) room 150 RecitationMonday 3:304:20 pm, 60 5th Ave (CDS) room 150 Office hoursBrett: Monday 5:006:00 pm, 60 5th Ave (CDS) room 650 Grading policyHomework (30%) + Midterm (30%) + Project proposal (10%) + Project (30%) HomeworkWeekly homework will be due on Wednesday at midnight. The assignments should be submitted through Gradescope. The homework and solutions will be available 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. Late policy: You are allowed to hand in homework up to 2 days later than the deadline twice during the semester. Any other late homeworks will not be accepted, without exceptions. BooksWe will provide selfcontained notes. Some useful additional references are
