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Measure Theory for Scientists and Engineers

An introduction to measure theory and Lebesgue integration with a focus on topics that tend to be of the most utility in science and engineering. The three course series MATH 6579, 6580, and 6221 is designed to provide a high level mathematical background for engineers and scientists.

 

This course is equivalent to MATH 6337. Students should not be able to obtain credit for both MATH 6579 and MATH 6337.

Mathematical Foundations of Data Science

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

Undergraduate Research

Independent research conducted under the guidance of a faculty member.

College Algebra

Study of the properties of algebraic, exponential, and logarithmic functions as needed for pre-calculus and calculus.

Probability Theory

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.

Mathematical Problem Solving

Pass/Fail basis. This course is intended to teach general mathematical problem solving skills, and to prepare students to take the Putnam Examination.

Undergraduate Seminar

Pass/fail basis.

This course provides students with a broad exposure to areas of mathematics research through weekly speakers.

Probability and Statistics with Applications

Introduction to probability, probability distributions, point estimation, confidence intervals, hypothesis testing, linear regression and analysis of variance.

A Second Course on Linear Algebra

This course will cover important topics in linear algebra not usually discussed in a first-semester course, featuring a mixture of theory and applications.

Introduction to Discrete Mathematics

Mathematical logic and proof, mathematical induction, counting methods, recurrence relations, algorithms and complexity, graph theory and graph algorithms.

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