Statistical Learning

By providing students with a comprehensive introduction to statistical and machine learning models, this course equips them with powerful tools for making data-driven decisions in diverse fields, including business, healthcare, engineering, and beyond.

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Textbook: An Introduction to Statistical Learning with Appications in R Second Edition
Authors: Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
ISBN: 978-1-0716-1418-1 (eBook), 
      978-1-0716-1420-4 (Paperback), 
      978-1-0716-1417-4 (Hardcover)
Publisher: Springer
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Bookcover of An Introduction to Statistical Learning with Appications in R Second Edition.

This is a required course for the traditional biostatistics concentration of the M.S. program in Biostatistics and Health Analytics. It is also an elective for the M.P.H. program and the geospatial concentration of the M.S program. In this course, students will learn the fundamental concepts and techniques of statistical learning algorithms. They will gain an in-depth understanding of essential methods such as linear regression, logistic regression, and linear discriminant analysis, as well as learn how to evaluate and validate models using cross-validation and bootstrapping techniques. Additionally, students will acquire skills in model selection, including advanced techniques such as ridge regression and the LASSO. The course also covers nonlinear models, tree-based methods, and support vector machines, along with unsupervised learning methods such as principal components and k-means clustering. By working with real-world examples and hands-on exercises using the R programming language, students will gain practical experience to develop accurate and reliable statistical learning models.