Introduction to Statistical Learning
by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani · first published 2021
78% recommend20% neutral2% critical
Why HN recommends it
Accessible, applied introduction to statistical machine learning, with free PDF and R/Python labs; a lighter alternative to Elements of Statistical Learning for newcomers who want a solid stats foundation.
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…those principles. After reading this, I began to notice general patterns in data infrastructure, which helped me quickly grasp how new technologies worked. (most are variations on the same principles) * Introduction to Statistical Learning (James et al) and Applied Predictive Modeling (Kuhn et al). These two books gave me a grand sweep of predictive modeling methods pre-deep learning, methods which continue to be useful and applicable to…
on “Ask HN: Which book or course gave you an unfair advantage?” ↗…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 That's a lot of material to cover. And at some point you should start experimenting…
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” ↗…want a more mathematical/statistical take on Machine Learning, then the two books by Hastie/Tibshirani et al are definitely worth a read (plus, they're free to download from the authors' websites!): Introduction to Statistical Learning: http://www-bcf.usc.edu/~gareth/ISL/ The Elements of Statistical Learning: http://statweb.stanford.edu/~tibs/ElemStatLearn/ Obviously there is the whole field of "deep learning" as well! A good place…
on “Ask HN: What are some good Machine Learning resources?” ↗…write your own numerical code anyway; someone else did it, and they handled all the edge cases that a naive implementation misses. You can find PDFs of the James, Witten, Hastie, Tibshirani book "An Introduction to Statistical Learning" [1]. Scroll on through - there is nothing intimidating math wise. All the heavy lifting is left to R. Jump in, the water is fine! [1] http://web.stanford.edu/~hastie/pub.htm
on “Complete Course on Machine Learning” ↗James, Witten, Hastie, and Tibshirani, "An Introduction to Statistical Learning." Available for download: http://faculty.marshall.usc.edu/gareth-james/ISL/ Taylor and Karlin, "An Introduction to Stochastic Modeling"
on “Ask HN: Recommend Books on Statistics” ↗I have Deep Learning with Python (Chollet, first edition) and Hands-On Machine Learning (Géron, first edition). Both books are highly recommended. Introduction to Statistical Learning is also available for free online: http://faculty.marshall.usc.edu/gareth-james/ISL/ Although I only read a few chapters from that book, I really like it (but I would have preferred a python version…
on “Best Data Science Books According to the Experts” ↗…https://www.amazon.ca/gp/product/3030334589/ - Probability: A Graduate Course (Allan Gut): https://www.amazon.ca/gp/product/1461447070/ - Statistical Rethinking (McElreath): https://www.amazon.ca/gp/product/036713991X/ - An Introduction to Statistical Learning (James, Witten, Hastie & Tibshirani): https://www.amazon.ca/Introduction-Statistical-Learning-Appl... - Regression and Other Stories (Gelman, Hill & Vehtari): https://www.amazon.ca/gp/product/1107676517/…
on “Ask HN: What do you read for general knowledge?” ↗1. Fundamentals of Physics by Halliday, Resnick, Walker 2. Quantum Mechanics by Eisberg, Resnick 3. Mathematical Physics by Arfken, Weber, Harris 4. Introduction to Statistical Learning with R by Hastie et al. 5. Deep Learning for Coders by Jeremy Howard
on “Ask HN: Name 3-5 books that had the most impact on your career and knowledge?” ↗…Techniques" by Koller. On the NLP front there's the standard texts "Speech and Language Processing" by Jurafsky and "Foundations of Statistical Natural Language Processing" by Manning. I also like "An Introduction to Statistical Learning" by James, Witten, Hastie and Tibshirani.
on “Level-Up Your Machine Learning” ↗These slide tutorials are excellent: engaging and friendly but still rigorous enough that they can be used as reference materials. They're a great companion to "Introduction to Statistical Learning" and "The Elements of Statistical Learning" by Hastie, Tibshirani, et al. The author of these tutorials is Andrew Moore, Dean of the School of Computer Science at Carnegie Mellon.
on “Statistical Data Mining Tutorials (2005)” ↗Introduction to Statistical Learning is free and quite good: http://www-bcf.usc.edu/~gareth/ISL/ Follow it up with Elements of Statistical Learning by three of the same authors for more advanced stuff.
on “Advantages of Using R Notebooks for Data Analysis Instead of Jupyter Notebooks” ↗if you want to focus on Statistical Learning, I can recommend Introduction to Statistical Learning Using R[1] by Prof. Hastie and Tibshirani. [1]: https://lagunita.stanford.edu/courses/HumanitiesSciences/Sta...
on “Ask HN: What are the best MOOCs you've taken?” ↗…first-year PhD students." But given that they distribute the PDF for free it's worth checking out. Hastie, Tibshirani & Friedman's The Elements of Statistical Learning and the watered-down and more practical Introduction to Statistical Learning are also nice. All of them can be downloaded from https://web.stanford.edu/~hastie/pub.htm
on “Ask HN: What are your favorite statistics and probability textbooks?” ↗…you want to do image processing and deep reinforcement learning. But there are lots of other domains. For tabular data (which is probably most relevant in Pharma, and probably the best place to start) Introduction to Statistical Learning by Hastie et al and Max Kuhn's Applied Predictive modelling cover a lot of the classical techniques. For univariate time series forecasting "Forecasting Principles and Practice" is great. For natural…
on “Ask HN: Full-on machine learning for 2020, what are the best resources?” ↗