I am a Ph.D student in Electrical Engineering. I am going to study the field of machine learning and I found some textbooks to study this field.
1) Probabilistic Graphical Models: Principles and Techniques by Koller
2) Bayesian Reasoning and Machine Learning by Barber
3) Machine Learning: A Probabilistic Perspective by Murphy
I know statistics a little bit because I studied Bayesian learning for my master degree in EE and I took some math courses such as stochastic processes and probability. But, if a book is too comprehensive and succinct, I have no ability to following the book. I would like to study machine learning in detail in terms of statistical learning. In this case, which book is good for me? Thanks.