NONPARAMETRIC STATISTICS AND MACHINE LEARNING METHODS

Name: NONPARAMETRIC STATISTICS AND MACHINE LEARNING METHODS
Availability: OutOfStock
Rating: 4.9 (33 reviews)

STAT 841

과목 설명

Statistical function estimation and classification; reproducing kernel machines, support vector machines; high dimensional model selection and estimation; Bayesian, empirical Bayesian interpretation of nonparametric learning methods; log density ANOVA and graphical models; tree ensemble methods including bagging, boosting, and random forest.

선수과목

STAT 610 or MATH/STAT 710

충족 요건

This course does not satisfy any prerequisites.

학점

미보고

개설 시기

미보고

평점

3.83

-3.07% 과거 데이터 대비

수료율

100%

과거 데이터 대비 변화 없음

A 비율

66.67%

-26.67% 과거 데이터 대비

학급 규모

-45.45% 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.

STATISTICAL INFERENCE FOR DATA SCIENCE

STAT 612

Statistical models, methods and theory for parameter estimation, Bayesian approach to parameter estimation, methods and theory for hypothesis tests, confidence sets, two-sample testing and ANOVA, categorical data analysis, linear regression.

ADVANCED STATISTICAL METHODS

STAT 849

Gain thorough grounding in modern statistical methods and theory. Classical and modern statistical techniques, including linear regression models, diagnostic tools, prediction, generalized linear regression models, random effect models, experimental design, and model selection. Apply these techniques to dataset in specific contexts.

STATISTICAL METHODS FOR DATA SCIENCE

STAT 613

Provides a thorough grounding in modern statistical methods. Introduces statistical techniques and methods of data analysis, including data description, linear regression models, diagnostic tools, prediction and model selection, and experimental design.

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.