DSC 291: Numerical Linear Algebra for Data ScienceTuTh 9:30am11:00am, WLH 2114 Announcements
Instructor: Alex Cloninger
Overview:This course will cover algorithms and theory in linear algebra, with a focus on data science applications. The course will only assume familiarity with an undergraduate course in linear algebra and matrices, and basic familiarity with Python and/or basic scientific programming. Topics will include: Linear algebraic systems, least squares problems and regularization, orthogonalization methods, illconditioned problems, eigenvalue and singular value decomposition, principal component analysis, structured matrix factorization and fast algorithms, randomized linear algebra, JL lemma, sparse approximations. A schedule of the course can be found below. There is not a textbook for this course, however there are several books that can be useful for reference:
Grades:There will be three small projects throughout the course of the quarter. These will be meant to gain practical knowledge and use of the topics discussed in the course, and to gain exposure to some of the data science applications of linear algebra. These projects will be graded on completeness, correctness, and clarity of the notebook/writeup. At the end of the quarter, students will be asked to form small groups and delve deeper into one project of interest, draw connections to existing research, and have a short presentation for the class. Project topics:
Schedule:
