CMSC498M - Foundations of Data Science
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|
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.
Hours Per Week
Recommended Prior Experience
Projects, Exams, or other Assessments
This course is in no way equivalent to CMSC320 nor will it satisfy the requirement for Data Science students.