# 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.

4 credits
Prereqs:
CSCI-2200 Foundations Of Computer Sci
and
MATH-2010 Multivar Calc & Matrix Algebra

# Past Term Data
Offered
Not Offered
Offered as Cross-Listing Only
No Term Data
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| Spring | Summer | Fall
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| (Session 1) | (Session 2) |

2026

Math Fndtns Of Machine Lrning (4c)

- Mohammad Mohammadi Amiri

Seats Taken: 74/90

2025

Math Fndtns Of Machine Lrning (4c)

- Mohammad Mohammadi Amiri

Seats Taken: 58/90

Math Fndtns Of Machine Lrning (4c)

- Daniel John DiTursi

Seats Taken: 60/60

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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