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This text misses two very important sub-areas of statistical learning, so much so that I can't believe they gave their textbook such a broad name ("statistical
by fizixer 11y ago
This text misses two very important sub-areas of statistical learning, so much so that I can't believe they gave their textbook such a broad name ("statistical learning", more like a subset of it. The whole book doesn't have a single mention of the word "markov". Seriously disappointed.).
- Graphical models
- Probabilistic logic, probabilistic programming, bayesian data analysis, etc, etc.
Can anyone point to good introductory texts?
I know for the first, Koller's text is highly recommmended but I'm not sure if it's at the introductory level.
- Aqwis 11y agoAs someone in the field, I don't think there are any texts at below the level of Koller/Friedman. Universities rarely if ever teach graphical models to students below the level at which Koller/Friedman is appropriate.
- platz 11y agoIm not sure graphical models should be the default tool people reach for
- IndianAstronaut 11y agoImplementation of these graphical models in R is dkne really well with the gRain package.
- dragandj 11y agoKruschke - Doing Bayesian Data Analysis (2nd ed)
- Eridrus 11y agoThere's a coursera course on Probabilistic Graphical Models: https://www.coursera.org/course/pgm https://www.coursera.org/course/pgm I would guess that's more approachable than a text book, but who knows.