Learn all the Theory underlying Machine Learning and Data Mining with the most contrasted book on the topic: The Elements of Statistical Learning!
The following is a review of the book The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) by Trevor Hastie, Robert Tibshriani and Jerome Friedman.
Review
As mentioned in the title, The Elements of Statistical Learning is seen by many Gurus as the Bible of Machine Learning. This second edition was published in 2009, and despite being an old text, it remains as the king of books to become a serious expert in the theory underlying Machine Learning.
It is a very conceptual and theoretical book, where many examples are given, and it comes with very illustrative and high-quality figures. It covers topics that go from Supervised and Unsupervised learning to Artificial Neural Networks, Support Vector Machines, Decision Trees and much much more.
This major new edition features many topics not covered in the original, including graphical models, Random forests, Ensemble methods, least angle regression & LASSO (one of the authors, Tibshirani, is actually the creator of this regularisation technique), non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for “wide” data (number of features being larger than the number of data points), including multiple testing and false discovery rates.
We want to make it very clear that The Elements of Statistical Learning is a highly theoretical book, it does not speak about programming, and the maths required to understand it is that of a medium-high level.
It is therefore not a book we would recommend to beginners on statistics, or for those that are looking to learn to implement Machine Learning algorithms in R or Python. Rather, we think this book is best for those that have a good statistics and mathematics foundation, that have been implementing and working with Machine Learning for a while, and that want to scale their knowledge or the theoretical concepts and the magic underlying the different algorithms.
If you are looking for a text to get into Machine Learning and the needed statistics with a better balance between theory and practice, we advice going for ‘Introduction to Statistical Learning with R’, by the same authors. You can find it here. Another good book with great content in Python this time, is ‘Hands on Machine Learning with Scikit – Learn, Keras and Tensorflow’. You can find a wonderful review of that book here.
Despite being very theoretical, The Elements of Statistical Learning avoids spinning around on the same topic or tedious and long demonstrations, going straight to the point in each subject, which makes it a great reference manual to refresh the deepest corners of Machine Learning algorithms. Because of this it is a great document to have for both researchers and those that use Machine Learning techniques in the business world.
Overall, we think it is a must have manual in the shelf of anybody who aims to be a Machine Learning expert, and definitely a good resource to reach cutting edge knowledge in this field.
Content
The contents of the book are the following:
- Introduction
- Overview of Supervised Learning
- Linear Methods for Regression
- Linear Methods for Classification
- Basis Expansions and Regularization
- Kernel Smoothing Methods
- Model Assessment and Selection
- Model Inference and Averaging
- Additive Models, Trees, and Related Methods
- Boosting and Additive Trees
- Neural Networks
- Support Vector Machines and Flexible Discriminants
- Prototype Methods and Nearest-Neighbors
- Unsupervised Learning
- Random Forests
- Ensemble LearningT
- Undirected Graphical Models
- High-Dimensional Problems: p N
The Elements of Statistical Learning can be found for free on PDF, however it is a text that if acquired is worth having on Paper in your work place: The Elements of Statistical Learning PDF.
Official book website: https://web.stanford.edu/~hastie/ElemStatLearn/
Summary
The Elements of Statistical Learning is the go-to book where many top academics will point when asked which is the best machine learning book about the theory, concepts, and workings of the algorithms and techniques.
Remember, it is neither a beginner nor a practical book. If you want a beginner book to Machine Learning we have reviews of the following, which might better fit your goals:
- The 100 Page Machine Learning book: Quite theoretical book, but clear and concise.
- Hands-On Machine Learning with Scikit-Learn & TensorFlow: Good balance between theory and theory and practice in Python.
- Python Machine Learning: Very practical book to learn Machine Learning implementations in Python.
You can buy The Elements of Statistical Learning on Amazon here:
The Elements of Statistical Learning
- This refurbished product is tested and certified to work properly. The product will have minor blemishes and/or light scratches. The refurbishing process includes functionality testing, basic cleaning, inspection, and repackaging. The product ships with all relevant accessories, and may arrive in a generic box.
- Hardcover Book
- Hastie, Trevor (Author)
- English (Publication Language)
- 767 Pages - 03/01/2021 (Publication Date) - Springer (Publisher)
Thank you for reading How to Learn Machine Learning and have a fantastic day!
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