MATRIX METHODS IN MACHINE LEARNING

Name: MATRIX METHODS IN MACHINE LEARNING
Availability: OutOfStock
Rating: 4.3 (2248 reviews)

COMPSCI/ECE/ME 532

과목 설명

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.

선수과목

(MATH 234 , MATH 320 , MATH 340 , MATH 341 , or MATH 375 ) and (ECE 203 , COMPSCI 200 , COMPSCI 220 , COMPSCI 300 , 301, 302, COMPSCI 310 , COMPSCI 320 , or placement into COMPSCI 300 ), graduate/professional standing, or declared in Capstone Certificate in Computer Sciences for Professionals

충족 요건

MATH 535 , ECE/ISYE 570 , and COMPSCI/ECE 561

학점

미보고

개설 시기

미보고

평점

3.01

-12.09% 과거 데이터 대비

수료율

94.55%

-3.65% 과거 데이터 대비

A 비율

20%

-57.42% 과거 데이터 대비

학급 규모

110

-11.92% 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.

APPLIED LINEAR ALGEBRA

MATH 443

Review of matrix algebra. Simultaneous linear equations, linear dependence and rank, vector space, eigenvalues and eigenvectors, diagonalization, quadratic forms, inner product spaces, norms, canonical forms. Discussion of numerical aspects and applications in the sciences.

LINEAR ALGEBRA AND OPTIMIZATION

MATH 345

Introduction to linear algebra, differential calculus in several variables, and basic optimization theory with applications to data science and related topics. Vectors, analytic geometry, matrices, linear functions, linear independence, orthogonality, inverses, partial derivatives and gradients, Taylor approximation, gradient descent, Lagrange multipliers, clustering, regression, classification. Implementation in Python.

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.

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.