Indeed, I can imagine (and still grossly underestimate) how frantically busy you would be. All the best for your startup. As I said, it was just a friendly ribb

Indeed, I can imagine (and still grossly underestimate) how frantically busy you would be. All the best for your startup. As I said, it was just a friendly ribbing. Given our interactions on HN I have come to gauge your taste a bit, hence my recommendations. Vapnik, V. N. (1998), Statistical Learning Theory is a tome. You probably would not have time to read this. I would say go with the other two books of his. They sort of summarize his body of early work. Again a lot of water has flown below the Vapnik bridge, but you will get a non-hyped view of what ML is about. Kevin P. Murphy, Machine Learning: A Probabilistic Perspective is also a hefty read, but its a good source and quite complementary to Vapnik's treatise. Kevin Murphy's book is about how to tractably model joint distribution of a (mostly discrete) set of random variables and draw inferences from them. So this is about smart and efficient ways to marginalize and condition and compress. Shai Shalev-Shwartz and Shai Ben-David, Understanding Machine Learning From Theory to Algorithms, 2014 This is also a great book. You will see a lot of KTC here but mostly within the framework of convexity and duality.