About This Course
This is an introductory-level course in supervised learning, with a
focus on regression and classification methods. The syllabus
includes: linear and polynomial regression, logistic regression and
linear discriminant analysis; cross-validation and the bootstrap,
model selection and regularization methods (ridge and lasso);
nonlinear models, splines and generalized additive models; tree-based
methods, random forests and boosting; support-vector machines. Some
unsupervised learning methods are discussed: principal components and
clustering (k-means and hierarchical).
This is not a math-heavy class, so we try and describe the methods
without heavy reliance on formulas and complex mathematics. We focus
on what we consider to be the important elements of modern data
analysis. Computing is done in R. There are lectures devoted to R,
giving tutorials from the ground up, and progressing with more
detailed sessions that implement the techniques in each chapter.
The lectures cover all the material in
An Introduction to Statistical
Learning, with Applications in R by James, Witten, Hastie and
Tibshirani (Springer, 2013). The pdf for this book is available for free on the book website.