MATHEMATICAL METHODS IN DATA SCIENCE

MATH 535

Course Description

A rigorous introduction to mathematical concepts important for modern data science. Topics include: matrix factorizations, optimization theory and algorithms, probabilistic models, finite Markov chains. Mathematical techniques are motivated by and illustrated on a range of applied problems from machine learning and statistics.

Prerequisties

(MATH 320 , MATH 340 , MATH 341 , MATH 375 or COMPSCI/ECE/ME 532 ) and (MATH/STAT 309 , MATH/STAT 431 , MATH 531 , STAT 311 or ECE 331 ) and (MATH 322 , MATH 341 , MATH 375 , MATH 421 , MATH 467 , or COMPSCI 577 ), graduate/professional standing, or member of Pre-Masters Mathematics (Visiting Intl) Prgrm

Satisfies

This course does not satisfy any prerequisites.

Credits

Offered

Not Applicable

Grade Point Average

3.46

1.64% from Historical

Completion Rate

94.87%

-1.6% from Historical

A Rate

64.1%

19.12% from Historical

Class Size

-21.3% from Historical

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

YUKUN YUE

[email protected]

1.7

Rating

Similar Courses

MATRIX METHODS IN MACHINE LEARNING

COMPSCI/ECE/ME 532

Linear algebraic foundations of machine learning featuring real-world applications of matrix methods from classification and clustering to denoising and data analysis. Mathematical topics include: linear equations, regression, regularization, the singular value decomposition, and iterative algorithms. Machine learning topics include: the lasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. Previous exposure to numerical computing (e.g. Matlab, Python, Julia, R) required.

TOPICS IN MATHEMATICAL DATA SCIENCE

ECE/MATH/STAT 888

Advanced topics in the mathematical foundations of data science

THEORETICAL FOUNDATIONS OF MACHINE LEARNING

COMPSCI/ECE/STAT 861

Advanced mathematical theory and methods of machine learning. Statistical learning theory, Vapnik-Chevronenkis Theory, model selection, high-dimensional models, nonparametric methods, probabilistic analysis, optimization, learning paradigms.

THEORY & ALGORITHMS FOR DATA SCIENCE

COMPSCI 541

Theoretical methods for data science. Topics include: review of probability background, concentration inequalities, geometry of high dimensional random variables, parametric and non-parametric estimation, selected topics from optimization (optimality conditions; deterministic and stochastic gradient descent), PAC learning, sample complexity and algorithms for linear classification and regression, and property/distribution testing. Uses Python programming language.

MATHEMATICAL FOUNDATIONS OF MACHINE LEARNING

COMPSCI/ECE 761

Mathematical foundations of machine learning theory and algorithms. Probabilistic, algebraic, and geometric models and representations of data, mathematical analysis of state-of-the-art learning algorithms and optimization methods, and applications of machine learning. Knowledge of probability [such asMATH/STAT 431orCOMP SCI/E C E 561] and linear algebra [such asMATH 341orM E/COMP SCI/E C E 532] is required.