elements-statistical-learning-notes

Summary notes and examples for every chapter in the popular textbook "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.

These Jupyter notebooks are meant to assist with study by summarizing the key points of each chapter, and by providing some code examples to support the text.

Further, they serve as useful reference material for the machine learning practitioner whenever a quick refresh or inspiration is needed on a particular topic.

Instructions:

Create a new Python 3 virtual environment.
Activate your virtual environment and pip install -r requirements.txt (included in this repo).
Run the jupyter lab command in your activated virtual environment.

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Name		Name	Last commit message	Last commit date
Latest commit History 4 Commits
data		data
support		support
.gitignore		.gitignore
Chapter 01 - Introduction.ipynb		Chapter 01 - Introduction.ipynb
Chapter 02 - Overview of Supervised Learning.ipynb		Chapter 02 - Overview of Supervised Learning.ipynb
Chapter 03 - Linear Methods For Regression.ipynb		Chapter 03 - Linear Methods For Regression.ipynb
Chapter 04 - Linear Methods for Classification.ipynb		Chapter 04 - Linear Methods for Classification.ipynb
Chapter 05 - Basis Expansion and Regularization.ipynb		Chapter 05 - Basis Expansion and Regularization.ipynb
Chapter 06 - Kernel Smoothing Methods.ipynb		Chapter 06 - Kernel Smoothing Methods.ipynb
Chapter 07 - Model Assessment and Selection.ipynb		Chapter 07 - Model Assessment and Selection.ipynb
Chapter 08 - Model Inference and Averaging.ipynb		Chapter 08 - Model Inference and Averaging.ipynb
Chapter 09 - Additive Models, Trees, and Related Methods.ipynb		Chapter 09 - Additive Models, Trees, and Related Methods.ipynb
Chapter 10 - Boosting and Additive Trees.ipynb		Chapter 10 - Boosting and Additive Trees.ipynb
Chapter 11 - Neural Networks.ipynb		Chapter 11 - Neural Networks.ipynb
Chapter 12 - Support Vector Machines and Flexible Discriminants.ipynb		Chapter 12 - Support Vector Machines and Flexible Discriminants.ipynb
Chapter 13 - Prototype Methods and Nearest Neighbors.ipynb		Chapter 13 - Prototype Methods and Nearest Neighbors.ipynb
Chapter 14 - Unsupervised Learning.ipynb		Chapter 14 - Unsupervised Learning.ipynb
Chapter 15 - Random Forests.ipynb		Chapter 15 - Random Forests.ipynb
Chapter 16 - Ensemble Learning.ipynb		Chapter 16 - Ensemble Learning.ipynb
Chapter 17 - Undirected Graphical Models.ipynb		Chapter 17 - Undirected Graphical Models.ipynb
Chapter 18 - High Dimensional Problems.ipynb		Chapter 18 - High Dimensional Problems.ipynb
README.md		README.md
requirements.txt		requirements.txt

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