Probabilistic Machine Learning
by Kevin P. Murphy · first published 2022
77% recommend23% neutral
Why HN recommends it
Broad, math-forward treatment of ML from a Bayesian view, with a background appendix praised for cutting the fluff. Suits CS people heading into ML research who want probability and math in one book.
Summarised by Claude from recommending comments
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…Great content, but poor printing (very low contrast, etc). This is not a debate on ebooks vs paper books, or PDF vs something else. It's about quality of printed books. I paid $$$ for Kevin Murphy's _Probabilistic Machine Learning_ from MIT Press. Excellent quality paper, great printing. It's a keeper. I'll be reading it years from today. Just as I read (and have) books from decades ago. I might pass 'em on to somebody well deserving,…
on “The enshittification of Amazon paperback books” ↗Probably the best applied math "tutorial" without all the bullshit I've yet seen is the mathematical background appendix in Probabilistic Machine Learning: An Introduction by Kevin P. Murphy.
on “Mathematics Cheat Sheet [pdf]” ↗I am a big fan of Murphy's ML book [1]. [1] https://smile.amazon.com/Probabilistic-Machine-Learning-Intr... It covers almost all the math you'd need to start doing ML research. I find it to be the ideal 'one book to rule them all' book for CS people in ML. Although, pure math grads in ML might find…
on “The big six matrix factorizations” ↗…techniques can probably be fruitfully applied, especially if you have some domain knowledge that the math/CS PhDs don’t have. For OP - I’m in a similar situation and have been going through Kevin Murphy’s “Probabilistic Machine Learning”, which is pretty massive and dense but also very lucid.
on “Ask HN: How to get back into AI?” ↗Machine learning is an enormous field, and if you are after a explanation/exploration from the ground up, then Kevin Murphy's Probabilistic Machine Learning: An Introduction is very good, but a bit of a tome. If instead, you want to focus on neural networks, I found Michael Nielsen's Neural Networks and Deep Learning, available at http://neuralnetworksanddeeplearning.com/,…
on “Ask HN: Tutorials written with heavy dependencies” ↗…https://www.brendangregg.com/bpf-performance-tools-book.html Furthermore, nothing keeps you from reading books about topics peripheral to computer science, say to keep up with the general vibes: - https://www.amazon.com/Probabilistic-Machine-Learning-Introd... - https://www.amazon.com/Deep-Learning-Foundations-Christopher... - https://www.amazon.com/Joy-Abstraction-Exploration-Category-... I find that all of these contribute significantly to my…
on “Programmers Don't Read Books – But You Should (2008)” ↗4 trustworthy pdfs of very high quality that I've used as resources to fill my gaps: - The 3 Kevin Murphy textbooks on Probabilistic Machine Learning - Deep Learning by Goodfellow et al I think they do contain some of the nuggets you're looking for, and have explanations too. I typically just use the index to find what I'm looking for but you could…
on “Ask HN: Resources on Stats for MLEs?” ↗…beginner coming from a programming background. If you want something more from the ML world that heavily emphasizes the benefits of probabilistic (Bayesian) methods, I highly recommend Kevin Murphy’s Probabilistic Machine Learning. I have only read the first edition before he split it into two volumes and expanded it, but I’ve only heard good things about the new volumes too.
on “Bayesian Data Analysis, Third edition (2013) [pdf]” ↗And to close the triad of machine learning bibles, don't forget Murphy's, which will apparently be extended soon! https://probml.github.io/pml-book/
on “The Elements of Statistical Learning [pdf]” ↗…institution near you and see if they have an undergraduate reading group on deep learning that is open to the public to attend. Mine does, and it's really helpful for staying up to date with the latest ideas. "Probabilistic Machine Learning" by Murphy contains the material that I would consider prerequisite if you want to understand the ideas which underpin modern deep learning (even if it contains virtually no deep learning in it), and…
on “The 90-year-old idea behind JEPA models: Canonical Correlation Analysis” ↗https://probml.github.io/pml-book/ Murphy's should be more than sufficient. Mackay (Information Theory, Inference, and Learning Algorithms) and Bishop (Pattern Recognition and Machine Learning) are also commonly recommended but their books are all more than a decade old.
on “Machine Learning Algorithms Cheat Sheet” ↗The original 2012 book was awesome! Would love to get my hands on the draft for "Probabilistic Machine Learning: Advanced Topics". https://probml.github.io/pml-book/book2.html
on “Probabilistic Machine Learning: An Introduction” ↗I'd suggest these two by Kevin Murphy: Probabilistic Machine Learning: An Introduction https://probml.github.io/pml-book/book1.html Probabilistic Machine Learning: Advanced Topics https://probml.github.io/pml-book/book2.html
on “Ask HN: What are the foundational texts for learning about AI/ML/NN?” ↗"Probabilistic Machine Learning" by Kevin Murphy is all you need. ... only half joking ;) https://probml.github.io/pml-book/
on “Bayesian statistics and machine learning: How do they differ?” ↗https://d2l.ai (Zhang et al) is a very nice book with the right amount of theory and tons of code. If you want a bit more than just DL, and better foundations, https://probml.github.io/pml-book (Murphy) is the place to begin with. Personally, I think a broader perspective, such as the one offered by the second one (Murphy) is the way to go.
on “How to get started learning modern AI?” ↗