Machine learning
by Kevin P. Murphy · first published 2012
54% recommend46% neutral
Why HN recommends it
Rigorous, theory-first textbook that builds a principled grasp of ML fundamentals through a probabilistic lens, with broad topic coverage. Suits readers comfortable with math who want depth over quick implementation.
Summarised by Claude from recommending comments
"This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and
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…tutorials but none of it tells you the whole path. Here are the resources I found useful: ========================================== Advices from Open AI, Facebook AI leaders Courses You MUST Take: Machine Learning by Andrew Ng (https://www.coursera.org/learn/machine-learning) /// Class notes: (http://holehouse.org/mlclass/index.html) Yaser Abu-Mostafa’s Machine Learning course which focuses much more on theory…
on “Ask HN: Why there is no Codecademy for ML or AI?” ↗Murphy's is probably most current, and an excellent text (read first review). OTW there's lots of stuff on the web for various levels of rigor http://www.amazon.com/Machine-Learning-Probabilistic-Perspec... http://metaoptimize.com/qa/questions/186/good-freely-availab... http://www.p-value.info/2012/11/free-datascience-books.html http://www.kaggle.com/wiki/Tutorials
on “A New Book: Building Machine Learning Systems with Python” ↗Here are the resources I found useful: ========================================== Advices from Open AI, Facebook AI leaders Courses You MUST Take: Machine Learning by Andrew Ng (https://www.coursera.org/learn/machine-learning) /// Class notes: (http://holehouse.org/mlclass/index.html) Yaser Abu-Mostafa’s Machine Learning course which focuses much more on theory…
on “Ask HN: Best way to get started with AI?” ↗Machine Learning: A Probabilistic Perspective by Kevin Murphy has a chapter on it. Or you can read the few papers from the 90s and early 00s on it.
on “Ask HN: Good resources to learn Variational Bayes method” ↗Two that I have enjoyed: 1. "Machine Learning" - Murphy It's a classic and a rigorous introduction to the subject. It focuses on theory over implementation so it's more for developing a principled understanding of machine learning fundamentals than…
on “Ask HN: Starting machine learning? Books, videos and other resources” ↗…possible removed? Do you mean extremely patient exposition? Do you mean a first principles approach that builds advanced probability theory, linear algebra and statistics from set theory, then dives into machine learning? Murphy’s Machine Learning: A Probabilistic Perspective is just over 1000 pages long. It’s an exceptionally good book for the mathematical theory behind machine learning. What would this book look like…
on “Book: Mathematics for Machine Learning” ↗Some of the best textbooks in statistics and machine learning: Applied ------- Hosmer et al., Applied Logistic Regression. An exhaustive guide to the perils and pitfalls of logistic regression. Logistic regression is the power tool of interpretable statistical…
on “Ask HN: What are the best textbooks in your field of expertise?” ↗There is a rather active community for Go in data science and machine learning. +1700 members on the #data-science channel on the Gopher slack, a few books on the topic, and a few libraries: Books Machine learning with Go - https://www.amazon.com/Machine-Learning-Classification-Time-...…
on “Differentiable programming for gradient-based machine learning” ↗Unfortunately, it's just something you start to notice once you become more familiar with the fundamental math underlying all of this. The book, Machine Learning: A Probabilistic Perspective by Kevin Murphy (the original book everyone in this thread is talking about) is probably the closest thing I can think of. Its goal is to frame everything around graphical…
on “Probabilistic Machine Learning: An Introduction” ↗His old book did just that: https://www.amazon.com/Machine-Learning-Probabilistic-Perspe...
on “Probabilistic Machine Learning: Advanced Topics” ↗It depends on the particular subtopics it covers. Machine Learning: A Probabilistic Perspective is from 2012 and it's still a great resource, although Murphy's newer book will certainly cover more up-to-date material.
on “Learning Theory from First Principles [pdf]” ↗it’s been a while since I’ve been a beginner so I might not have the best resources, but I would recommend Harvard’s Stat 110 with Joe Blitzstein (lectures online) and then Machine Learning by Kevin Murphy. might be a scarier book to someone not confident in their math, but overall a better one imo for something more directly comparable to the niche ISLR filled, Bishop’s books are generally…
on “Probabilistic Artificial Intelligence” ↗For a latest reference on AI and machine learning for network engineer please check this book by Javier Antich [1]. Please also check the review here [2]. For what it's worth, the book is listed in the "10 Books Every Network Engineer Should Read" [3].…
on “AI for Network Engineers: Understanding Flow, Flowlet, and Packet-Based LB” ↗David MacKay: Information Theory, Inference, and Learning Algorithms Kevin Murphy: Machine Learning: a probabilistic perspective. Christopher Bishop: Pattern Recognition and Macine Learning
on “Computational and Inferential Thinking – The Foundations of Data Science” ↗Kevin Murphy - "Machine Learning: A Probabilistic Perspective" is a great (literally - 1000+ pages) textbook that is basically self-contained (pre-reqs: some comfort w. multivariable calculus, linear algebra, basic computer science theory;…
on “Statistical Machine Learning, Spring 2016” ↗