> It's no secret that machine learning is mostly statistics and Bayesian probability, typically with a focus on prediction rather than explanation, but the dist

> It's no secret that machine learning is mostly statistics and Bayesian probability, typically with a focus on prediction rather than explanation, but the distinction is at best blurry. Well, apparently it's a "secret" to whomever selected the title of the book. Congrats on seeing that they are just trying to predict and not explain. Breiman came to that position a long time ago and explained his position. So, they are like Ptolemy and his circles within circles fitting the astronomical data on the planets and, then, predicting the motions of the planets instead of Newton who, with his calculus, law of gravity, and second law of motion, both predicted and explained the motion of the planets. With a lot of assumptions, some commonly justified in practice only with a flexible imagination, can do explanation -- to be believed only after believing the assumptions. A little better is the approach of factor analysis since do have orthogonality where, thus, really can identify the unique contributions that sum to the whole prediction. It's just that then the factors are super tough to explain. Since everyone sees that this applied math called computer science learning really is just some basic statistics, then why the heck is the HN community so eager to swallow taking some statistics, calling it computer science, and saying that it's about learning? > Bayesian probability I will give you some warm, wise, rock solid advice: Take "Bayesian probability" and drop it into the wet, round bowl and pull the chain. For anyone talking about it, do the same with them or at least their material. That's very much not the good stuff -- it's like farming by plowing with a wooden stick. Then learn about the central topic in probability, conditioning based on the Radon-Nikodym result, e.g., with a nice proof by von Neumann. Now you are up conditional expectation, regular conditional probabilities, Markov processes, martingales, the strong Markov property, and the serious stuff. E.g., for random variables X, Y, E[Y|X] is a random variable and the best non-linear least squares approximation of Y from X. And, for some collection A of random variables, possibly uncountably infinite, E[Y|sigma(A)], where sigma(A) is the sigma algebra generated by the set A, is the best least squares approximation possible from all the random variables, used jointly, in A. Really, it's more powerful to condition on a sigma algebra than directly on the random variables that generate the sigma algebra. And there's much more, e.g., sufficient statistics. With Bayesian, you are crawling; with conditioning you are flying supersonic. If want to argue with me about theft of statistics and misuse of learning, then you will need another source on dumping Bayesian for conditioning. For that, read a text on, say, graduate probability by any of, say, Chung, Breiman, Neveu, Loeve and any of a few more. > More over, saying an introductory course to anything is "stealing" is ridiculous. Sure, would be if didn't relabel it as learning and computer science. Call it Stat 101, 102, 201, 202 -- fine. Call it computer learning or some such -- theft and BS. I'm surprised at your willingness to swallow and smile at that theft of Stat 101, etc., and that just hype use of the word learning, both of which are just wildly inappropriate. Gee, wait until the computer science big data people discover sufficient statistics! The credit goes to the field of statistics -- give credit where it is due. Call the material by it's appropriate name -- statistics or multi-variate statistics or statistical hypothesis testing or resampling plans in statistics. The content is statistics and very definitely not computer science. E.g., a lot of the more recent content was directly from Breiman, and he was definitely not a computer scientist. When the social scientists, the biomedical scientists, and the agricultural scientists studied statistics, and they did study a lot of it, they called it, right, statistics. When the economists won Nobel prizes for applying linear programming, the called it linear programming. It is true that they called dual variables shadow prices. Similarly for quadratic programming (H. Markowitz). When the chemists used group representation theory in molecular spectroscopy, they called it, right, group representation theory. When the oil patch people were looking for oil by using the fast Fourier transform to do de-convolution of acoustic signals, they call it Fourier theory. Having computer science steal statistics and call it computer science learning is not the standard academic approach. There's something going on with computer science that is not good and not clear. This is really simple stuff. Somehow this simple stuff is controversial at HN. Not so good. Come on, guys -- there's a world outside the computer science department, and much of the best of the future of computing will come from that world and not anything within current computer science departments. E.g., for the last paper I published on a problem in computer science, the computer science chaired profs and journal Editors in Chief couldn't understand or review the math -- finally a EE prof could and did. BTW, when the high end EE guys work with stochastic integration, e.g., as in E. Wong's book, they call it stochastic integration. Amazing. Similarly for the high end finance guys.