Bayesian reasoning and machine learning
by David Barber · first published 2011
75% recommend25% neutral
Why HN recommends it
Thorough, systematic textbook on Bayesian methods and graphical models, dense but accessible, with a companion Matlab library. Free online, suited to self-studying readers wanting theory beyond a first ML course.
Summarised by Claude from recommending comments
"Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensi
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…Learning, by Hastie et all. It is free from Stanford. http://www-stat.stanford.edu/~tibs/ElemStatLearn Mining of Massive Datasets, free from Stanford. http://infolab.stanford.edu/~ullman/mmds.html Bayesian Reasoning and Machine Learning, by Barber, free 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…
on “A Tour of Machine Learning Algorithms” ↗…Analysis (Kruschke), aka "the puppy book": http://www.amazon.com/Doing-Bayesian-Data-Analysis-Second/dp... Bayesian Data Analysis (Gellman): http://www.amazon.com/Bayesian-Analysis-Chapman-Statistical-... Bayesian Reasoning and Machine Learning (Barber): http://www.amazon.com/Bayesian-Reasoning-Machine-Learning-Ba... Probabilistic Graphical Models (Koller et al): https://www.coursera.org/course/pgm http://www.amazon.com/Probabilistic-Graphical-Models-Princip...…
on “Ask HN: What are some good Machine Learning resources?” ↗…rating of the player goes up. Players are given problems close to their ratings, which keeps everyone happy. I believe they use Glicko to track uncertainty in the rating. Chapter 22 of David Barber's "Bayesian Reasoning and Machine Learning" (he makes it available online) does a nice (perhaps brief) job of explaining the progression through the Rasch model, the Bradley-Terry-Luce model and Elo. As an aside, the way they chesstempo generate…
on “How Khan Academy is using machine learning to assess student mastery” ↗Form my own journey I would say that a good place to start for graphical models might be "Bayesian Reasoning and Machine Learning" by Barber. It's free (http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=...). I haven't read through it, but I've heard good things. However, it doesn't cover some basic things like SVM, RVM,…
on “Level-Up Your Machine Learning” ↗I would add to that two classics with free PDFs available from the authors' websites: 1. David Barber: Bayesian Reasoning and Machine Learning 2. David MacKay: Information Theory, Inference, and Learning Algorithms [1] http://www.cs.ucl.ac.uk/staff/d.barber/brml/ [2] http://www.inference.phy.cam.ac.uk/mackay/itila/ Both require a willingness…
on “Probabilistic Programming & Bayesian Methods for Hackers” ↗Bayesian Reasoning and Machine Learning (BRML) by David Barber is an excellent introductory textbook. It's also free: http://www0.cs.ucl.ac.uk/staff/d.barber/brml/
on “Ask HN: Bayesian Statistics – where do I start?” ↗some useful books available as a PDF: David Barber "Bayesian Reasoning and Machine Learning" http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=... -- Thorough and systematic, accompanied by a Matlab library Marc Steyvers "Computational Statistics with Matlab" http://psiexp.ss.uci.edu/research/teachingP205C/205C.pdf --…
on “Stan: a probabilistic programming language” ↗If you can get through it on independent study, Bayesian Reasoning and Machine Learning by David Barber is incredible, dense, but very accessible. It's also free online! It does require matlab though.
on “Ask HN: How can a front-end developer dive into machine learning?” ↗Elements of Statistical Learning http://www-stat.stanford.edu/~tibs/ElemStatLearn/ Gaussian Processing for Machine Learning http://www.gaussianprocess.org/gpml/ Bayesian Reasoning and Machine Learning http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online Convex Optimization http://www.stanford.edu/~boyd/cvxbook/
on “Free pdf Books on Machine/Statistical Learning” ↗It's hard to say exactly, but if you can work through all the problems in Barber's Bayesian Reasoning and Machine Learning, and some other standard 'frequentist' stats text, you'd be well placed to get started. My profile says where I work.
on “From PhD to Data Scientist: Tips for Making the Transition” ↗…you wade into a new language (e.g. Learn you some Haskell for great good, if you are new to Haskell). - Computer science-y books: Buy them all! (e.g. Hackers delight, introduction to algorithms, bayesian reasoning and machine learning, purely functional data structures)
on “Ask HN: How useful is buying a book for learning a framework / technology?” ↗…have a bit on this (although quite dense material): http://www.gatsby.ucl.ac.uk/teaching/courses/ml1-2011/lect5b... as part of http://www.gatsby.ucl.ac.uk/teaching/courses/ml1-2011.html I quite like "Bayesian reasoning and machine learning" too: http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/090310.pdf
on “The main trick in machine learning” ↗…Optimization http://www.e-booksdirectory.com/details.php?ebook=9575 A Course in Machine Learning http://www.e-booksdirectory.com/details.php?ebook=9395 A First Encounter with Machine Learning http://www.e-booksdirectory.com/details.php?ebook=8818 Bayesian Reasoning and Machine Learning http://www.e-booksdirectory.com/details.php?ebook=5283 Introduction to Machine Learning http://www.e-booksdirectory.com/details.php?ebook=4493 The Elements of Statistical Learning: Data Mining, Inference,…
on “Free eBooks On Machine Learning” ↗Andrew Ng's Coursera course simply titled "Machine Learning" is good - it addresses the mathematics of fundamental algorithms and concepts while giving practical examples and applications: https://www.coursera.org/learn/machine-learning Regarding books, there are many very high quality textbooks available (legitimately) for free online: Introduction to Statistical Learning (James et al., 2014) h
on “Ask HN: What is the right way to self-learn Machine learning?” ↗Try Bayesian Reasoning and Machine Learning specifically, as these are all about Bayesian reasoning.
on “A visual introduction to Gaussian Belief Propagation” ↗