Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)

Author: Daphne Koller, Nir Friedman
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by aabajian   2022-02-24
Didn't read the document, but hopefully it mentions PageRank, the prime example of using probabilistic graphical models to rank nodes in a directed graph. More info: https://www.amazon.com/Probabilistic-Graphical-Models-Princi...

I've heard that Google and Baidu essentially started at the same time, with the same algorithm discovery (PageRank). Maybe someone can comment on if there was idea sharing or if both teams derived it independently.

by boltzmannbrain   2019-01-03
> 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 by training, but self-taught programmer and AI researcher. I've been working in industry as an 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

by lowglow   2017-08-19
I used "Probabilistic Graphical Models" By Koller/Friedman

[0] https://www.amazon.com/Probabilistic-Graphical-Models-Princi...