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LINEAR ALGEBRA AND OPTIMIZATION

MATH 345
课程描述

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.

先修课程

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 .

满足要求
学分

未报告

开课时间

未报告

平均绩点
3.12

与历史数据相比无变化

完成率
88.24%

与历史数据相比无变化

A率
35.29%

与历史数据相比无变化

班级规模
17

与历史数据相比无变化

Cumulative Grade Distribution

教师 (2026 Summr)

按评分排序，数据来自 Rate My Professors

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LINEAR OPTIMIZATION

COMPSCI/ISYE/MATH/STAT 525

Introduces optimization problems whose constraints are expressed by linear inequalities. Develops geometric and algebraic insights into the structure of the problem, with an emphasis on formal proofs. Presents the theory behind the simplex method, the main algorithm used to solve linear optimization problems. Explores duality theory and theorems of the alternatives.