Foundations of Machine Learning, by Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar, is a 2012 book on machine learning theory. Understanding Machine Learning: From Theory to Algorithms, by Shai Shalev-Shwartz and Shai Ben-David, is a similar 2014 book that's fairly well-known and targeted a little more introductory than Mohri/Rostamizadeh/Talwalkar, but still has lots of theory in it. Neural Network Learning: Theoretical Foundations, by Martin Anthony and Peter Bartlett, is a 1999 book about ML theory phrased as being about neural networks, but (to my impression not having read it) is mostly about ML theory in general. These three books mostly take the predominant viewpoint of statistical learning theory. There is also an interesting point of view called computational learning theory, inspired more by computer science theory.