I am looking for a book about machine learning that would suit my physics background. I am more or less familiar with classical and complex analysis, theory of probability, сcalculus of variations, matrix algebra, etc. However I have not studied topology, measure theory, group theory and other more advanced topics. I try to find a book that is written neither for beginners, nor for mathematicians.
Recently I have read great book "Statistical inference" written by Casella and Berger. They write in Introduction that "The purpose of this book is to build theoretical statistics (as different from mathematical statistics) from the first principles of probability theory". So I am looking for some "theoretical book" about machine learning.
There are many online courses and brilliant books out there that focus on the practical side of applying machine learning models and using the appropriate libraries. It seems to me that there are no problems with them, but I would like to find a book on theory.
By now I have skimmed through following books:
It looks very nice the only point of concern is that the book was published in 2006. So I am not sure about the relevance of the chapters considering Neural nets since this field is developing rather fast.
This book also seems very good. It covers most of topics as well as the first book. However I am feeling that its style is different and I do not which book will suit me better.
This one covers more recent topics such as natural language processing. As far as I understand it represents the view of computer scientist on the machine learning.
Maybe it has a slight bias towards probability theory which is stated in the title. However book looks fascinating as well.
I think that the first or the second book should suit me but I do not know what decision to make.
I am sure that I have overlooked some books. Are there some other ML books that focus on theory?