ST563 - Introduction to Statistical Learning
- Prerequisites: ST 512 or 514 or 515 or 517
- Term & Frequency: Spring and Summer II
- Student Audience: Graduate students in all fields
- Credit: 3 credits
- Recent Texts: An Introduction to Statistical Learning with Applications in R, by James, Witten, Hastie, and Tibshirani (2013).
- Recent Instructors: Howard Bondell
- Background and Goals: The focus of this course is on regression and classification methods for applied supervised learning. Knowledge of multivariable regression and applied statistics is expected.
- Content: The focus of this course is on regression and classification methods for applied supervised learning. Topics covered will include linear and polynomial regression, logistic regression and linear discriminant analysis, cross-validation and the bootstrap, model selection and regularization methods, splines and generalized additive models, nearest neighbor and tree-based methods, random forests and boosting, and support-vector machines. Unsupervised learning methods such as principal components and hierarchical clustering will also be discussed. The R software language will be used in the course, but no prior experience with R is required.
- Subsequent Courses: ST 564
SP 2017 Sections:
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