Pattern Recognition and Machine Learning
by Christopher M. Bishop · first published 2008
77% recommend22% neutral1% critical
Why HN recommends it
Rigorous, math-heavy textbook on probabilistic machine learning, praised for clear explanations of Bayesian methods, neural networks and graphical models; suits readers with some math who want depth beyond intro courses.
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In machine learning, hands down these are some of the best related textbooks: - [0] Pattern Recognition and Machine Learning (Information Science and Statistics) and also: - [1] The Elements of Statistical Learning - [2] Reinforcement Learning: An Introduction by Barto and Sutton - [3] The Deep Learning by Aaron Courville, Ian Goodfellow, and Yoshua Bengio…
on “Ask HN: What are the best textbooks in your field of expertise?” ↗> study textbooks. Do exercises. Treat it like academic studying This. Highly recommend Russel & Norvig [1] for high-level intuition and motivation. Then Bishop's "Pattern Recognition and Machine Learning" [2] and Koller's PGM book [3] for the fundamentals. Avoid MOOCs, but there are useful lecture videos, e.g. Hugo Larochelle on belief propagation [4]. FWIW this is coming from a mechanical engineer…
on “Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?” ↗…available online. http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=... Learning from data, by Abu-Mostafa. It comes with Caltech video lectures: http://work.caltech.edu/telecourse.html Pattern Recognition and Machine Learning, by Bischop http://research.microsoft.com/en-us/um/people/cmbishop/prml/ Also noteworthy Information Theory, Inference, and Learning Algorithms, by Mackay, free. http://www.inference.phy.cam.ac.uk/itprnn/book.html…
on “A Tour of Machine Learning Algorithms” ↗…Data (Flach): http://www.amazon.com/Machine-Learning-Science-Algorithms-Se... Machine Learning: A Probabilistic Perspective (Murphy): http://www.amazon.com/Machine-Learning-Probabilistic-Perspec... Pattern Recognition and Machine Learning (Bishop): http://www.amazon.com/Pattern-Recognition-Learning-Informati... There are some great resources/books for Bayesian statistics and graphical models. I've listed them in (approximate) order of…
on “Ask HN: What are some good Machine Learning resources?” ↗Why would you say we don't understand how these systems work? Stochastic gradient descent, for example, is not particularly enigmatic. Pattern Recognition and Machine Learning by Christopher Bishop is a good place to start if you want to gain an understanding of how and why machine learning algorithms work.
on “Richard Feynman Computer Heuristics Lecture (1985) [video]” ↗…Learning (https://smile.amazon.com/Python-Machine-Learning-Sebastian-R...) * An Introduction to Statistical Learning (https://smile.amazon.com/Introduction-Statistical-Learning-A...) Intermediate: * Pattern Recognition and Machine Learning (https://smile.amazon.com/Pattern-Recognition-Learning-Inform...) * Machine Learning: A Probabilistic Perspective (https://smile.amazon.com/Machine-Learning-Probabilistic-Pers...) * All of Statistics:…
on “Machine Learning for Software Engineers” ↗…are relations between two numbers (like "is bigger than" or "is the following number of"), but a neural network can, since addition is part of a NN. My personal recommendation on machine learning is 'Pattern Recognition and Machine Learning' by Chris Bishop. But you definately do need a solid mathematical background for that.
on “How to teach a Bayesian spam filter to play chess” ↗…nicely with the Weka open source ML toolkit http://www.cs.waikato.ac.nz/ml/weka/ (although it is a good read without the toolkit) If you want a bit more math, I really like the recent (Oct 2007) book: Pattern Recognition and Machine Learning by Christopher M. Bishop http://www.amazon.com/Pattern-Recognition-Learning-Informati... It is nicely self contained, going through all the stats you'll need.
on “Can anyone recommend good books, articles or essays that introduce machine learning?” ↗…scratch the surface, and it does a disservice to the reader to provide noplace to go. Here's a few to get started: 1. Russel and Norvig's AI text, 2. Elements of Statistical Learning by Hastie et. al., 3. Pattern Recognition and Machine Learning by Chris Bishop. On the other hand going right into code examples is useful, including jumping right into getting real data downloaded and worked on.
on “Critique: "Programming Collective Intelligence"” ↗…implement algorithms I suggest that you make use of the machine learning at ocw.mit.edu Alternatively, if you want a good dose of a theoretical explanation of algorithms currently in use I highly recommend "Pattern Recognition and Machine Learning" by Christopher Bishop. It is definitely the best machine learning (and statistics) textbook that I have ever come across.
on “Ask HN: Good books on machine learning?” ↗Of the top of my head, - Machine Learning by Tom M Mitchell http://www.cs.cmu.edu/~tom/mlbook.html For general reading and introductions I also like: - Pattern Classification by Richard Duda - Pattern Recognition and Machine Learning by Christopher Bishop For a bit more emphasis on statistics and math, I usually dive in to - Classification,Parameter Estimation and State Estimation by van der Heijden And last, but certainly not…
on “Think Stats: Probability and Statistics for Programmers” ↗…one of the best resources for learning about Bayesian ML methods if you need a gentle introduction. The only other book I found which was similarly clear and well thought-out is Christopher Bishop's "Pattern Recognition and Machine Learning".
on “Free book on Bayesian machine learning by David Barber” ↗Pattern Recognition and Machine Learning by Christopher M. Bishop Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) by Kevin Murphy
on “Numenta releases brain-derived learning algorithm package NuPIC” ↗…After you really can understand Andrew Ng's course notes I'd recommend a textbook because they go in more detail and cover more topics. My two favorites for general statistical machine learning are: * Pattern Recognition and Machine Learning by Christopher M. Bishop * The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman Both are very intensive, perhaps to a fault. But they are good references and…
on “A Course in Machine Learning” ↗It depends on your math background, but I've found Yaser S. Abu-Mostafa's Learning From Data the best, although Christopher M. Bishop's Pattern Recognition and Machine Learning is the gold standard. ;-)
on “A Tour of Machine Learning Algorithms” ↗