The Elements of Statistical Learning
by Trevor Hastie, Robert Tibshirani, Jerome Friedman · first published 2001
63% recommend33% neutral4% critical
Why HN recommends it
Graduate-level, theory-heavy reference on statistical machine learning, available free online. Suits readers with a math background who want the whys behind methods; often paired with the gentler ISL as a first step.
Common criticism: Some find it too mathematical for beginners
Summarised by Claude from recommending comments
Describes important statistical ideas in machine learning, data mining, and bioinformatics. Covers a broad range, from supervised learning (prediction), to unsupervised learning, including classification trees, neural networks, and support vector machines.
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…tooth now, but it is still one of the most approachable and well written books in the field. Another old book that stands up very well is "Probability Theory: the Logic of Science" by E. T. Jaynes. "Elements of Statistical Learning" by Tibshirani is also good. "Bayesian Data Analysis" by Andrew Gelman is another great read. "Deep Learning" by Ian Goodfellow and Yoshua Bengio is useful for getting caught up with recent advances…
on “Ask HN: What maths are critical to pursuing ML/AI?” ↗For those who want a more solid take on machine learning, and who still remember their math and probability/statistics, (i.e. advanced undergrad or new grad student), the best texts seem to be: The Elements of Statistical Learning by Hastie, Tibshirani and Friedman, available for free on line. Pattern Recognition and Machine Learning by Chris Bishop. Very Bayesian. Machine Learning: A Probabilistic Perspective by Kevin Murphy.…
on “Probabilistic Programming & Bayesian Methods for Hackers” ↗…to becoming a data scientist. Some of it isn't. The Coursera courses are great for getting you started; they're entry-level college courses, and if you read the papers and the seminal textbooks (e.g. Elements of Statistical Learning by Hastie et al) you can get into the intermediate territory in a couple years or so. It's not easy, but it can definitely be done. Getting to the expert level, I think, just requires real-world experience…
on “Machine-Learning Maestro Michael Jordan on the Delusions of Big Data and Others” ↗* Elements of Statistical Learning - Hastie, Tibshisrani * (Lot's of machine learning books to list: PRML, All of Stats, Deep Learning, etc.) * Active Portfolio Management - Kahn, Grinold * Thinking, fast and slow - Kahneman * Protein…
on “Ask HN: Best books you read in the past decade?” ↗In my opinion (MSc math, PhD in applied statistics, currently postdoc at epidemiology department), the fundamentals are books like The Elements of Statistical Learning: https://hastie.su.domains/Papers/ESLII.pdf I feel like the term data science is completely useless. Machine learning is an approach to "do AI" through statistics. Specifically, it is a branch of statistics…
on “What Is Machine Learning Anyway?” ↗…with better advice. But, there are two books I really feel should not be missed. For statistics: "Statistics, Third Edition" David Freedman, Robert Pisani, Roger Purves. For machine learning: "The Elements of Statistical Learning" Trevor Hastie, Robert Tibshirani and Jerome Friedman.
on “Ask HN: I want to learn statistics and data mining” ↗I'm a math geek, but I'm also a mostly self-taught data scientist. "The Elements of Statistical Learning" (https://web.stanford.edu/~hastie/local.ftp/Springer/OLD/ESLI...) is far and away the best book I've seen. It took me hundreds of hours to get through it, but if you're looking to understand things…
on “Understanding Machine Learning: From Theory to Algorithms (2014)” ↗My favorite textbook: Elements of Statistical Learning by Hastie. It's free, too! If you don't understand something in the book, back up and learn the pre-reqs as needed. http://web.stanford.edu/~hastie/ElemStatLearn/printings/ESLI...
on “Ask HN: How to get started with machine learning?” ↗…difficult - Khan Academy Precalculus (they also have Linear Algebra and Calculus material). This gives you a math foundation. Some books more specific to ML: - Foundations of Data Science - Blum et al. - Elements of Statistical Learning - Hastie et al. The simpler version of this book - Introduction to Statistical Learning - also has a free companion course on Stanford's website. - Machine Learning: A Probabilistic Perspective - Murphy…
on “Ask HN: Full-on machine learning for 2020, what are the best resources?” ↗Introduction to Statistical Learning https://faculty.marshall.usc.edu/gareth-james/ISL/ Elements of Statistical Learning https://web.stanford.edu/~hastie/ElemStatLearn/ Machine Learning: A Probabilistic Perspective https://mitpress.mit.edu/books/machine-learning-1
on “Introduction to Support Vector Machines in Machine Learning” ↗…Algorithms" by David MacKaye http://www.inference.org.uk/itprnn/book.pdf "Probability Theory: the Logic of Science" by E. T. Jaynes http://www.med.mcgill.ca/epidemiology/hanley/bios601/Gaussia... "Elements of Statistical Learning" by Tibshirani https://web.stanford.edu/~hastie/Papers/ESLII.pdf "Bayesian Data Analysis" by Andrew Gelman http://hbanaszak.mjr.uw.edu.pl/TempTxt/(Chapman%20&%20Hall_C...
on “Ask HN: What maths are critical to pursuing ML/AI?” ↗…site mentions "An Introduction to Statistical Learning": The first book on statistical learning by Hastie, Tibshirani and Friedman, which is absolutely terrific, is freely available for download: The Elements of Statistical Learning http://web.stanford.edu/~hastie/ElemStatLearn/
on “Machine Learning Books That Helped Me Level Up” ↗…big fan of ISL - one of the best intro machine-learning oriented textbooks out there IMO. If you're looking for book that still offers a broad survey while going a bit deeper into the math, I recommend Elements of Statistical Learning as well (they share 2 authors): https://web.stanford.edu/~hastie/Papers/ESLII.pdf
on “Introduction to Statistical Learning” ↗…advanced interview question involving regression, I would expect companies to jealously guard it. If you're earnestly interested in building and testing your knowledge, I would recommend you read The Elements of Statistical Learning and Data Analysis Using Regression and Multilevel/Hierarchical Models. Also a good upper undergrad textbook in probability, like A First Course in Probability.
on “Data science interview questions with answers” ↗If you are looking for machine learning outside of Deep Learning, there are just 2 books 1. Elements of Statistical Learning (very frequentist treatment) by Hastie et.all [1] 2. Pattern Recognition and Machine Learning by Bishop(for a Bayesian treatment)[2] Both are freely available online. Reading one book will get you to…
on “Ask HN: In 2022, what is the proper way to get into machine/deep learning?” ↗