This course introduces students to the essential methods and tools important for Mathematics and Computing. It provides a comprehensive introduction to numerical methods fundamental in modeling, simulation and machine learning. This course will help students to establish an understanding of algorithmic approaches to tackle real-life problems involving matrix computations, optimization, and differential equations.
The following table contains a list of all undergraduate special topics courses offered by the School of Math within the last 5 years. More information on courses offered in the current/upcoming semester follows below.
The following table contains a list of all graduate special topics courses offered by the School of Math within the last 5 years. More information on courses offered in the current/upcoming semester follows below.
Modern data science methods and the mathematical foundations: linear regression, classification and clustering, kernel methods, regression trees and ensemble methods, dimension reduction.
This course is a mathematical introduction to probability theory, covering random variables, moments, multivariate distributions, law of large numbers, central limit theorem, and large deviations.
MATH 3215, MATH 3235, and MATH 3670 are mutually exclusive; students may not hold credit for more than one of these courses.
Introduction to probability, probability distributions, point estimation, confidence intervals, hypothesis testing, linear regression and analysis of variance.
MATH 3215, MATH 3235, and MATH 3670 are mutually exclusive; students may not hold credit for more than one of these courses.
This course will cover important topics in linear algebra not usually discussed in a first-semester course, featuring a mixture of theory and applications.
