Bayesian reasoning and machine learning

What Hacker News says

1. jstx1Aug 2022
   Have you taken undergraduate-level linear algebra, multivariable calculus and probability? Those are the prerequisites if you want to approach things a bit more rigorously. If you've covered them, get something specific to ML.

   I like Hands-On ML... by Geron as a decent intro to ML book. FastAI seems a bit overrated to me - I didn't like that it uses its own helper library or the teaching style but it obviously works for other people.

   Then there's more exhaustive books on theory - Elements of Statistical Learning, Pattern Recognition and Machine Learning, Bayesian Reasoning and Machine Learning, Murphy's books on probabilistic ML etc. But obviously the theory books have a lot of overlap with each other so there will be lots of material to skip after you've read one or two of them.

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2. iflpAug 2021
   Try Bayesian Reasoning and Machine Learning specifically, as these are all about Bayesian reasoning.
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3. mipmap04May 2017
   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.

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4. indigentmartianJan 2017
   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) http://www-bcf.usc.edu/~gareth/ISL/

   the above book shares some authors with the denser and more in-depth/advanced

   The Elements of Statistical Learning (Hastie et al., 2009) http://statweb.stanford.edu/~tibs/ElemStatLearn/

   Information Theory: Inference & Learning Algorithms (MacKay, 2003) http://www.inference.phy.cam.ac.uk/itila/p0.html

   Bayesian Reasoning & Machine Learning (Barber, 2012) http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=...

   Deep Learning (Goodfellowet al., 2016) http://www.deeplearningbook.org/

   Reinforcement Learning: An Introduction (Sutton & Barto, 1998) http://webdocs.cs.ualberta.ca/~sutton/book/ebook/the-book.ht...

   ^^ the above books are used on many graduate courses in machine learning and are varied in their approach and readability, but go deep into the fundamentals and theory of machine learning. Most contain primers on the relevant maths, too, so you can either use these to brush up on what you already know or as a starting point look for more relevant maths materials.

   If you want more practical books/courses, more machine-learning focussed data science books can be helpful. For trying out what you've learned, Kaggle is great for providing data sets and problems.

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5. kristjankalmSep 2015
   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 -- Somewhat unfinished but very good hands-on guide for a total novice

   David Draper "Bayesian Modeling, Inference and Prediction" https://users.soe.ucsc.edu/~draper/draper-BMIP-dec2005.pdf -- Quite good if you're already familiar with a field, might be slightly daunting for a beginner

   and the classic, David MacKay's "Information Theory, Inference, and Learning Algorithms" http://www.inference.phy.cam.ac.uk/itprnn/book.html

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6. shogunmikeMay 2015
   Some good books on Machine Learning:

   Machine Learning: The Art and Science of Algorithms that Make Sense of 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 increasing difficulty/mathematical complexity:

   Think Bayes (Downey): http://www.amazon.com/Think-Bayes-Allen-B-Downey/dp/14493707...

   Bayesian Methods for Hackers (Davidson-Pilon et al): https://github.com/CamDavidsonPilon/Probabilistic-Programmin...

   Doing Bayesian Data 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...

   If you 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 to start is with: http://deeplearning.net/

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