Curriculum
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Descriptions
IEMS 304: Statistical Learning for Data Analysis


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Description

PREREQUISITIES
GEN_ENG 150, 231, 241 and IEMS 302;  or equivalents; IEMS 303 recommended

 FOR AY 2026–2027
COMP_SCI 150 or GEN_ENG 150; IEMS 302; GEN_ENG 231 or IEMS 303; GEN_ENG 241 or ES_APPM 245

Predictive modeling of data using modern regression and classification methods. Multiple linear regression; logistic regression; pitfalls and diagnostics; nonparametric and nonlinear regression and classification such as trees, nearest neighbors, neural networks, and ensemble methods.

  • This course counts as an IE/OR elective for Industrial Engineering.

LEARNING OBJECTIVES

  • Understand common data structures in modern predictive and explanatory modeling problems in business, engineering and the sciences and how to formulate the most appropriate solutions
  • Learn R statistical software basics and how to use it for regression and classification problems
  • Develop ability to fit appropriate linear and logistic regression models, including model selection and diagnostics
  • Develop ability to interpret fitted linear and logistic regression models for explanatory and predictive purposes
  • Learn fundamental concepts in nonlinear regression and classification, including maximum likelihood estimation, cross-validation, ridge and lasso shrinkage
  • Learn how to fit and interpret popular supervised learning models including trees, smoothers, nearest neighbors, random forests, and boosted trees

TOPICS

  • Multiple linear regression basics: model fitting, statistical inference, prediction
  • Multiple linear regression: influence, residual diagnostics, multicollinearity, interactions, categorical predictors, variable selection, model evaluation criteria, ridge and lasso regression
  • Logistic regression: model fitting and interpretation, statistical inference, diagnostics
  • Nonlinear regression basics: maximum likelihood estimation, nonlinear least squares, cross-validation, bootstrapping
  • Classification and regression trees
  • Nearest neighbors for classification and regression
  • Boosted trees and random forests
  • R statistical software throughout the course

MATERIALS
Required: An Introduction to Statistical Learning with Applications in R, by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani, Springer. ISBN 978-1-4614-7138-7. Electronic version available free.

Recommended: IEMS 304 Reference Guide