I am a math master student and have done fundamental math courses like probability theory, measure theory, linear algebra and know a little bit about functional analysis. What is good way for me to learn machine learning in depth?
I have read the classical text Pattern-Recognition and Machine Learning last summer; my impression was that it was very ineffective to read the book chapter by chapter like a mathematical text. The book does not go deep enough for many algorithms and skip too many steps considered too technical by engineers.
Is there a machine learning book that maybe does not cover too many topics, but treat each one in depth and takes advantage of math when necessary? It will be great to be able connect fundamental mathematical objects with machine learning (I am thinking about Lp spaces, hilbert space etc).