현재 이 사이트는 활발히 개발 중입니다. 문제를 발견하시면 신고해 주세요! - 문제 신고하기

MATHEMATICAL FOUNDATIONS OF MACHINE LEARNING

COMPSCI/ECE 761
과목 설명

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.

선수과목

Graduate/professional standing

충족 요건
학점

미보고

개설 시기

미보고

평점
3.45

2.94% 과거 데이터 대비

수료율
97.5%

0.02% 과거 데이터 대비

A 비율
40%

-7.49% 과거 데이터 대비

학급 규모
40

-24.53% 과거 데이터 대비

Cumulative Grade Distribution

강사 (2026 Summr)

다음 사이트의 평점순으로 정렬 Rate My Professors

유사 과목

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.

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.

PROBABILITY AND INFORMATION THEORY IN MACHINE LEARNING

COMPSCI/ECE 561

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.

MATHEMATICAL METHODS IN DATA SCIENCE

MATH 535

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.

THEORETICAL FOUNDATIONS OF LARGE-SCALE MACHINE LEARNING

ECE 826

Mathematical foundations of large-scale machine learning and optimization. Focus on recent texts in machine learning, optimization, and randomized algorithms, focused on tradeoffs that are driving algorithmic design in this new discipline. These trade-offs revolve around speed of convergence, statistical accuracy, robustness, scalability, algorithmic complexity, and implementation.