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> 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

I would also include some books about statistics. Two excellent introductory books are:

Statistical Rethinking https://www.amazon.com/Statistical-Rethinking-Bayesian-Examp...

An Introduction to Statistical Learning http://www-bcf.usc.edu/~gareth/ISL/

Oof those are all dense reads for a new comer... For a first dip into the waters I usually suggest Introduction to Statistical Learning. Then from there move into PRML or ESL. Were you first introduced to core ML through Bishop? +1 for a solid reading list.

PGMs were in fashion in 2012, but by 2014 when Deep Learning had become all the rage, I think PGMs almost disappeared from the picture. Do people even remember PGMs exist now in 2019?

You'll find plate models, PGM junk, etc in modern papers on explicit density generative models and factorizing latents on such models.

Fashion is relevant only if you want to approach it as a fashion industry.

PGMs also provide the intuition behind GANs and variational autoencoders.

Hands up for Bishop and Russel Norvig.

Russel Norvig should be treated as a subtle intro to AI.

The start Bishop to understand concepts.

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