The book is available at https://teazrq.github.io/SMLR/. It is currently serving as textbook and supplementary material for three courses I teach at UIUC:

• STAT 432: Basics of Statistical Learning
• STAT 546: Machine Learning in Data Science
• STAT 542: Statistical Learning

This book currently covers the following topics:

1. Basic Knowledge
   • R, R Studio and R Markdown
   • Linear regression and linear algebra
   • Numerical optimization basics
2. Model Selection and Regularization in Linear Models
   • Ridge regression
   • Lasso
   • Spline
3. Classification models
   • Logistic regression
   • Discriminant analysis
4. Nonparametric Models with Local Smoothing
   • K-nearest neighbor
   • Kernel smoothing
5. Kernel Methods and RKHS
   • Support vector machine
   • RKHS
   • Kernel ridge regression
6. Tree and Ensemble Models
   • Tree models
   • Random forests
   • Boosting
7. Unsupervised Learning
   • K-means
   • Hierarchical clustering
   • PCA
   • self-organizing map
   • Spectral clustering
   • UMAP