Follow up reading on ML

[GB449]

I am looking for good books / lectures on Bayesian theory, Graphical models, Aggregation and Reinforcement.

Bayesian Reasoning and Machine Learning by David Barber appears to be a good book. I am interested in both theory and practice. I want to use ML professionally as a data scientist (which I am not right now), so practical would be better. I have a Math. background, so always appreciate the theory but I want to use ML at work. Any recommendations for packages and books will be appreciated. I recently purchased Data Mining with R by Luis Torga.

Thank you!

[yaser]

Originally Posted by GB449

I am looking for good books / lectures on Bayesian theory, Graphical models, Aggregation and Reinforcement.

Bayesian Reasoning and Machine Learning by David Barber appears to be a good book. I am interested in both theory and practice. I want to use ML professionally as a data scientist (which I am not right now), so practical would be better. I have a Math. background, so always appreciate the theory but I want to use ML at work. Any recommendations for packages and books will be appreciated. I recently purchased Data Mining with R by Luis Torga.

Thank you!

Some of my colleagues have told me that "videolectures.net" contains some good tutorial lectures on some of the topics.