# Math Fndtns Of Machine Lrning

## CSCI-2210

This course covers the essential building blocks of machine learning, focusing on topics in linear algebra, continuous probability and stochastic, and optimization. This provides students with foundational mathematical concepts to the components of machine learning - data, models, and learning algorithms - at an introductory level, emphasizing their basic functionalities and relationships. These mathematical foundations are the bedrock upon which machine learning is constructed. The topics that will be covered in this course are: Vectors, matrices, matrix operations and decomposition, eigenvalues, eigenvectors, vector calculus, calculating gradients of functions of vectors and matrices, probability theory, and linear regression.

1 credit
Prereqs:
none

# Past Term Data
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2001 | Programming In Lisp (1c) Daniel M. Manthey Seats Taken: 39/120 |  |
2000 | Programming In Lisp (1c) Daniel M. Manthey Seats Taken: 24/120 |  | Programming In Lisp (1c) Daniel M. Manthey Seats Taken: 19/120
1999 | Programming In Lisp (1c) Alok K Mehta Seats Taken: 23/120 |  | Programming In Lisp (1c) Daniel M. Manthey Seats Taken: 13/120
1998 |  |  | Programming In Lisp (1c) Kenneth William Flynn Seats Taken: 22/120