Resources

Primary textbook

An Introduction to Statistical Learning with Applications in Python (ISLP) James, Witten, Hastie, Tibshirani, Taylor

Available free online at statlearning.com

This is the main reference for the course. The Python edition includes code examples in scikit-learn and statsmodels.

Reading by lecture

Lecture	Topic	ISLP chapters
3	Preprocessing pipelines	Ch. 2 (Statistical Learning)
4	Linear models	Ch. 3 (Linear Regression)
5	Classification	Ch. 4 (Classification)
6	Bias-variance tradeoff	Ch. 2.2 (Bias-Variance)
7	Model selection	Ch. 5 (Resampling), Ch. 6 (Linear Model Selection)
9	Unsupervised / PCA	Ch. 12.1—12.2 (PCA)
10	Ensembles	Ch. 8 (Tree-Based Methods)
11	SVM	Ch. 9 (Support Vector Machines)
12	Boosting	Ch. 8.2.3 (Boosting)
13	Clustering	Ch. 12.4 (Clustering)
14	Neural networks	Ch. 10 (Deep Learning)

Supplementary resources

Online references

• scikit-learn User Guide — authoritative documentation for all models and pipelines used in this course
• Python Data Science Handbook — good introduction to pandas, numpy, and matplotlib
• Kaggle Learn — short practical courses on ML topics

Additional reading

• The Elements of Statistical Learning (Hastie, Tibshirani, Friedman) — a more mathematical treatment, free at web.stanford.edu/~hastie/ElemStatLearn/
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (Geron) — practical and code-heavy, good project reference