PROBABILITY AND INFORMATION THEORY IN MACHINE LEARNING

COMPSCI/ECE 561

Course Description

Probabilistic tools for machine learning and analysis of real-world datasets. Introductory topics include classification, regression, probability theory, decision theory and quantifying information with entropy, relative entropy and mutual information. Additional topics include naive Bayes, probabilistic graphical models, discriminant analysis, logistic regression, expectation maximization, source coding and variational inference.

Prerequisties

(MATH 320 , MATH 340 , MATH 341 , MATH 375 , or COMPSCI/ECE/ME 532 or concurrent enrollment) and (ECE 331 , MATH/STAT 309 , MATH/STAT 431 , STAT 311 , STAT 324 , ME/STAT 424 or MATH 531 ) or grad/profsnl standing or declared in Capstone Certificate in Computer Sciences for Professionals

Satisfies

This course does not satisfy any prerequisites.

Credits

Not Reported

Offered

Not Reported

Grade Point Average

Not Reported

Could not calculate change

Completion Rate

Not Reported

Could not calculate change

A Rate

Not Reported

Could not calculate change

Class Size

Not Reported

No change from Historical

No data available

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

Similar Courses

PROBABILITY AND RANDOM PROCESSES

ECE 730

Review of basic probability. Advanced probability concepts. Random vectors; linear filtering of random processes; stationarity; power spectral densities; estimation; convergence; Markov chains; Poisson process; Wiener process. Knowledge of basic probability analysis [such asE C E 331,STAT/MATH 431, orSTAT 311] strongly encouraged.

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.

INTRODUCTION TO MACHINE LEARNING AND STATISTICAL PATTERN CLASSIFICATION

STAT 451

Pattern classification, regression analysis, clustering, and dimensionality reduction. For each category, covers fundamental algorithms and selections of contemporary, current state-of-the-art algorithms. Focus on evaluation of machine learning models using statistical methods. Statistical pattern classification approaches, including maximum likelihood estimation and Bayesian decision theory, algorithmic and nonparametric approaches. Practical use of machine learning algorithms using open source libraries from the Python programming ecosystem.

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.