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

Author: Daphne Koller, Nir Friedman
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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...