This text is currently written as a supplement to ISL for use in STAT 432, Basics of Statistical Learning at The University of Illinois at Urbana-Champaign. Some additional details and additional topics are included, but the main focus in on providing more thorough R examples.
- March 2019 - Significant updates are expected during the Summer of 2019. In no particular order
- Move unsupervised learning before supervised learning
- Add mixture models and general comments on density estimation
- Add a "crash course" on ML at the very start of book.
- K-means for clustering
- RF for classification
- RF for regression
- Add a brief introduction to the idea of ML through the usual Pokemon example.