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Department:

MATH

Course Number:

4210

Hours - Lecture:

3

Hours - Lab:

0

Hours - Recitation:

0

Hours - Total Credit:

3

Typical Scheduling:

Every Fall

Modern data science methods and the mathematical foundations: linear regression, classification and clustering, kernel methods, regression trees and ensemble methods, dimension reduction.

Prerequisites:

Calculus I and II: Math 1551 and Math 1552; and Linear algebra, such as 1553 or 1554 or 1564; and Probability, such as Math 3215, 3225, 3235, 3670.

Course Text:

James, Gareth, Daniela Witten, Trevor Hastie and Robert Tibshirani, An introduction to statistical learning. 2nd edition, available online

Topic Outline:

Introduce modern data science techniques and the foundational mathematical concepts in linear algebra, probability, and basic optimization related with these techniques

· Teach how to use software to perform learning tasks while adequately addressing the practical challenges (e.g., modeling, parameter tuning, computation, and speed)

· Provide students with valuable first-hand experience in handling real and complex data