The trilogy is complete! My "Advanced Topics" book is officially released today. Buy it on Amazon, or get it for free at probml.github.io/pml-book/book2….
Kevin Patrick Murphy
1,119 posts
Kevin Patrick Murphy
@sirbayes
Research Scientist at Google DeepMind. Interested in Bayesian Machine Learning.
Joined October 2016
- I am delighted to announce that a draft of my latest book, “Probabilistic Machine Learning: Advanced Topics”, is now available online at probml.ai. It covers #DeepGenerativeModels, #BayesianInference, #Causality, #ReinforcementLearning, #DistributionShift, etc.
- I am happy to announce that the first draft of my RL tutorial is now available. arxiv.org/abs/2412.05265
- I just got a hardcopy of my own book - it finally feels real :) I'm very happy with the print quality (see attached photo) - but you can still get the pdf for free from probml.ai if you prefer.
- I am delighted to announce that the camera-ready version of my new book, "Machine Learning: Advanced Topics", is finally available online for free at probml.github.io/book2 (@mitpress will publish the hard copy in 2023.)
- I am pleased to announce that the camera ready version of my new textbook, "Probabilistic Machine Learning: An Introduction", is finally available from probml.ai. Hardcopies will be available from MIT Press in Feb 2022.
- I am delighted to announce that my new book, “Probabilistic Machine Learning: An Introduction”, is finally available in print format! You can order it from mitpress.mit.edu/books/probabil…, or from Amazon. Also available at probml.ai 1/4
- I am pleased to announce a new version of my RL tutorial. Major update to the LLM chapter (eg DPO, GRPO, thinking), minor updates to the MARL and MBRL chapters and various sections (eg offline RL, DPG, etc). Enjoy! arxiv.org/abs/2412.05265
- I am delighted to announce that the "real" camera-ready version of my new book, "Probabilistic Machine Learning: Advanced Topics", is now available. It will appear in print this summer, but it is already freely available online at probml.github.io/book2.
- I'm happy to announce that v2 of my RL tutorial is now online. I added a new chapter on multi-agent RL, and improved the sections on 'RL as inference' and 'RL+LLMs' (although latter is still WIP), fixed some typos, etc.
- A draft of the sequel, "Probabilistic Machine Learning: Advanced Topics", will be released by late Fall 2021. Together these two new books amount to about ~2000 pages of content. So my 2012 book ("Machine Learning: a Probabilistic Perspective") has finally been surpassed!
- While GenAI is fun, I think its economic value is grossly over estimated, because it’s unreliable, risky and expensive to make and serve. It’s fine for creative tasks, but not (yet) autonomous agentsAirline installs chatbot. Customer gets bad information from it. Customer asks for refund. Airline says "the chatbot is a separate legal entity that is responsible for its own actions" (!). Court says no. (Whew.) Airline makes refund and turns off chatbot. arstechnica.com/tech-policy/20…
- I'm pleased to announce another (fairly minor) update to my RL tutorial (I fixed some typos, cited more papers, and added a wee bit more stuff on multi-agent RL and RL for LLMs). I don't have time to work on this anymore, so enjoy it as is! (Link below)
- I like this paper. They prove that transformers are guaranteed to suffer from compounding errors when doing long reasoning chains (as @ylecun has argued), and much apparent "success" is just due to unreliable pattern matching / shortcut learning.
