An Introduction to Statistical Learning with Applications in Julia

• The Elements of Statistical Learning (ESL) by Hastie, Tibshirani, and Friedman (2001) has been a widely popular book for Machine Learning
• Introduction to Statistical Learning, With Applications in R (ISLR) (2013) by James, Witten, Hastie and Tibshirani provide a less technical more practical treatment to the topic
• Introduction to Statistical Learning, With Applications in Python (ISLP) (2023) by James, Witten, Hastie, Tibshirani and Taylor provide same contents as ISLR but implementation in Python

This repo is an attempt to provide the same contents with application in Julia. You can choose to read any of the book available in the site Statistical Learning.

The chapters cover the following topics:

1. Introduction
2. Statistical Learning
3. Regression
4. Classification
5. Resampling methods
6. Linear model selection and regularization
7. Moving beyond linearity
8. Tree-based methods
9. Support vector machines
10. Deep learning
11. Survival analysis and Censored Data
12. Unsupervised learning
13. Multiple testing