CMSC498M - Foundations of Data Science

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Course Information
Data science is a fast-expanding area that touches upon numerous disciplines. This class covers some of the mathematical and algorithmic foundations of this subject, including (but not limited to) aspects of high-dimensional geometry, best-fit subspaces and singular value decomposition, machine learning, and algorithms for massive data sets. Mathematical maturity and an interest in algorithmic and mathematical reasoning are essential prerequisites for this class. The freely-available textbook "Foundations of Data Science" by Blum, Hopcroft, and Kannan, available at http://www.cs.cornell.edu/jeh/book.pdf will be the textbook for the class.

Instructor Section Day & Time Location
Aravind Srinivasan 0101 TuTh 12:30pm - 1:45pm CSI 1122

Course Prerequisite(s)
Prerequisite: Minimum grade of C- in CMSC351; and permission of CMNS-Computer Science department. Or must be in the (Computer Science (Doctoral), Computer Science (Master's)) program.

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Misc Info
This course is in no way equivalent to CMSC320 nor will it satisfy the requirement for Data Science students.