About Probabilistic Graphical Models, is there book other than Daphne Koller's book that you would suggest?
Bishop's Pattern Recognition and Machine Learning has a chapter thats free online: https://www.microsoft.com/en-us/research/wp-content/uploads/...
Elements of Statistical Learning
Machine Learning: A Probabilistic Perspective
Especially the first 2 are rather the standard "intro to ML textbooks", with a frequentist focus (ISL may even have zero Bayesian stuff - Naive Bayes is not "Bayesian" – while ESL still has maybe 10% bayesian content if that).
Instead, I would suggest the following for learning Bayesian methods, especially given the HN crowd:
The former is a much recommended book since it's very comprehensive and builds everything from the ground up and was the basis for the entire course. The latter is a beast of it's own and we simply covered what was effectively the first chapter as part of the course.
- Doing Bayesian Data Analysis (dog book)
- Student's Guide to Bayesian Statistics
- Bayesian Data Analysis 3 (currently free! http://www.stat.columbia.edu/~gelman/book/)