Probabilistic Graphical Models
by Daphne Koller, Nir Friedman · first published 2009
43% recommend51% neutral5% critical
Why HN recommends it
Thorough, foundational textbook on graphical models, including a deeper treatment of MCMC than most. Suits readers who want a strong base for statistics and ML and pair it with Koller's Coursera course.
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…AI research engineer for ~6 years. [1] https://www.amazon.com/Artificial-Intelligence-Modern-Approa... [2] https://www.amazon.com/Pattern-Recognition-Learning-Informat... [3] https://www.amazon.com/Probabilistic-Graphical-Models-Princi... [4] https://youtu.be/-z5lKPHcumo
on “Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?” ↗…Parameter Estimation and State Estimation, by van der Heijden. http://prtools.org Computer Vision: Models, Learning, and Inference, by Prince, available for free http://www.computervisionmodels.com/ Probabilistic Graphical Models, by Koller. Has an accompanying course on Coursera.
on “A Tour of Machine Learning Algorithms” ↗…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…
on “Ask HN: What are some good Machine Learning resources?” ↗…Learning (http://www.deeplearningbook.org/ or https://smile.amazon.com/Deep-Learning-Adaptive-Computation-...) * Neural Networks and Deep Learning (http://neuralnetworksanddeeplearning.com/) Advanced: * Probabilistic Graphical Models: Principles and Techniques (https://smile.amazon.com/Probabilistic-Graphical-Models-Prin...) I have also found that looking into probabilistic programming is helpful too. These resources are pretty good:…
on “Machine Learning for Software Engineers” ↗It has much more than MCMC, but the "Probabilistic Graphical Models" textbook by Daphne Koller (of Coursera) and Nir Friedman is a thorough introduction to the subject. It includes a discussion of MCMC that will leave you with a deeper understanding than the typical shallow…
on “Free Python ebook: Bayesian Methods for Hackers” ↗…essentially similar algorithms using the same visual language of "graphical models." To learn more about that, see the book on "Information, Physics, and Computation," by Mezard and Montanari, the book on "Probabilistic Graphical Models," by Koller and Friedman, or my own articles ;-).
on “Why the name "dynamic programming"?” ↗…if I hadn't taken the time to read/think. Overall, I'm extremely happy with the time spent learning from Tim. That said, if you are looking for really hard challenges, you should take Daphne Koller's Probabilistic Graphical Models.
on “Coursera/Stanford online algorithms I course – a retrospective” ↗…David MacKay's Intro to Info Theory class: https://www.youtube.com/watch?v=BCiZc0n6COY There is a free book for it at: http://www.inference.org.uk/mackay/itila/ Daphne Koller's 3 coursara classes on Probabilistic Graphical Models are also really good.
on “Probabilistic Programming and Bayesian Methods for Hackers (2013)” ↗Probabilistic Graphical Models by Koller & Friedman. In anything statistics and ML related, being able to deal with complicated probabilitistic things that are all related is really useful. This book gives you that toolkit. It's a…
on “Ask HN: Math books that made you significantly better at math?” ↗I used to take a lot of courses on cousera and EdX. I still take some here and there, but not as many as before. Some of the courses are amazing and unbelievably rewarding, like Daphne Koller's Probabilistic Graphical Models, Robert Sedgewick's Analytical Combinatorics, and Gerald Sussman's course on system optimizations. I'm very grateful for such learning opportunities. Unfortunately over time, I also found that these courses…
on “The Triple Failure of 2U, EdX, and Axim” ↗…Elementary Probability Theory"[4] 5. Tao's "Ultraproducts as a Bridge Between Discrete and Continuous Analysis"[5] 6. Some canonical machine learning text, like Murphy[6] or Bishop[7] 7. Koller/Friedman's "Probabilistic Graphical Models"[8] 8. Lawvere's "Taking Categories Seriously"[9] From there you should see a variety of paths for mapping (things:Uncertainty) <-> (things:Structure). The Giry monad is just one of them, and would…
on “Physics, Topology, Logic and Computation: A Rosetta Stone (2009) [pdf]” ↗…class but it is still relevant for beginners.(https://work.caltech.edu/telecourse.html) Neural Networks and Deep Learning (Recommended by Google Brain Team) (http://neuralnetworksanddeeplearning.com/) Probabilistic Graphical Models (https://www.coursera.org/learn/probabilistic-graphical-model...) Computational Neuroscience (https://www.coursera.org/learn/computational-neuroscience) Statistical Machine Learning (http://www.stat.cmu.edu/~larry/=sml/)…
on “Ask HN: Why there is no Codecademy for ML or AI?” ↗…(http://www.ics.uci.edu/~mlearn/). Use Weka to run the standard algorithms from W+F on real data. After that things can get as complicated as you want. I'm personally betting that graphical models (see Koller's "Probabilistic Graphical Models") will continue to grow in importance. On the statistical side, I strongly recommend "Applied Linear Regression" by Weisberg to get a sense for the kinds of idea statisticians bring to the party (significance,…
on “Ask HN: Machine Learning? Where to start?” ↗…suggest "Foundations of Machine Learning" by Mohri. For a good reference for anything else I'd suggest "Machine Learning: A Probabilistic Perspective" by Murphy. For more depth on graphical models look at "Probabilistic Graphical Models: Principles and Techniques" by Koller. On the NLP front there's the standard texts "Speech and Language Processing" by Jurafsky and "Foundations of Statistical Natural Language Processing" by Manning.…
on “Level-Up Your Machine Learning” ↗…class but it is still relevant for beginners. (https://work.caltech.edu/telecourse.html) Neural Networks and Deep Learning (Recommended by Google Brain Team) (http://neuralnetworksanddeeplearning.com/) Probabilistic Graphical Models (https://www.coursera.org/learn/probabilistic-graphical-model...) Computational Neuroscience (https://www.coursera.org/learn/computational-neuroscience) Statistical Machine Learning (http://www.stat.cmu.edu/~larry/=sml/)…
on “Ask HN: Best way to get started with AI?” ↗