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LINEAR ALGEBRA AND OPTIMIZATION

MATH 345
Course Description

Introduction to linear algebra, differential calculus in several variables, and basic optimization theory with applications to data science and related topics. Vectors, analytic geometry, matrices, linear functions, linear independence, orthogonality, inverses, partial derivatives and gradients, Taylor approximation, gradient descent, Lagrange multipliers, clustering, regression, classification. Implementation in Python.

Prerequisites

MATH 222 and (COMPSCI 200 , COMPSCI 220 , COMPSCI 300 , COMPSCI 310 , COMPSCI 320 , or placement in COMPSCI 300 ). Not open to students with credit for MATH 320 , MATH 340 , MATH 341 , or MATH 375 .

Satisfies
Credits

Not Reported

Offered

Not Reported

Grade Point Average
3.12

No change from Historical

Completion Rate
88.24%

No change from Historical

A Rate
35.29%

No change from Historical

Class Size
17

No change from Historical

Cumulative Grade Distribution

Instructors (2026 Summr)

Sorted by ratings from Rate My Professors

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