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

Apr 16, 2013 - Phase II — Practitioners will really start to push the boundaries of modeling in fundmental ways in order to address many applications that don't fit well into the current machine learning, text mining, or graph analysis paradigms. 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. May 29, 2013 - Here, we explain some key aspects of machine learning that make it useful for genome annotation, with illustrative examples from ENCODE. ȿ两本书为纯理论教材，可以作为编写算法的理论依据。但是由于过于理论，不建议在理解算法的时候阅读。 网络教材：. Feb 15, 2014 - Pattern Recognition and Machine Learning（Bishop） 或Machine Learning, A Probabilistic Perspective. May 11, 2013 - Will Read Data Mining: Practical Machine Learning Tools and Techniques 难度低使用. Enter Paramveer Dhillon, a Penn Computer Science (machine learning) Ph.D. Student, who sent his paper, "A Risk Comparison of Ordinary Least Squares vs Ridge Regression" (with Dean Foster, Sham Kakade and Lyle Ungar). Will Read Machine Learning Mitchell 适合初学者. Machine Learning: a Probabilistic Perspective Kevin Patrick Murphy. Murphy KP: Machine Learning: A Probabilistic Perspective.