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Machine Learning: A Probabilistic Perspective Kevin P. Murphy
Publisher: MIT Press

Over the two weeks at Dr Hennig closed his talk with work on probabilistic numerics- taking the view that the numerical techniques used when an analytically solution is unavailable can be viewed as estimation and solved probabilistically. Just like Valiant (2009) framed evolution (and ecorithms more generally) as a formal subset of machine learning, algorithmic philosophy allows us to look at the act of scientific inquiry as a formal subset of machine learning. Sep 19, 2013 - I highly recommend anyone in machine learning to attend a summer school if possible(there's at least one every year, 3 planned for 2014) and other graduate students to see if their field runs a similar program. Based upon subsequent discussions and feedback, I've changed my view. Feb 24, 2014 - Not least, Frank DiTraglia at Penn sent some interesting links to the chemometrics literature, which prominently features PLS and has some interesting probabilistic perspectives on it. Jun 26, 2013 - As such, if we want to look at the philosophy of science, we should begin with an instrumentalist or operationalist perspective. Enter Paramveer Dhillon, a Penn Computer Science (machine learning) Ph.D. I'm also adding a reference for looking at probability from the Bayesian perspective. Probability can be very counter-intuitive. Dec 26, 2010 - In the previous list, I thought it would be good to recommend some lighter texts as introductions to topics like probability theory and machine learning. Student, who sent his paper, "A Risk Comparison of Ordinary Least Squares vs Ridge Regression" (with Dean Foster, Sham Kakade and Lyle Ungar). Straight into the deep end is the way to to choose from the probability list, in order to build a base in probability theory. The latter stance originated with Percy Williams Bridgman .