Chapter 9
[:1] Machine Learning Lecture 3 (CS 229) A. Ng – Stanford University 2008
[9:2] Matrix decompositions for regression analysis §1.2 The QR decomposition D Bates – 2007 - http://www.stat.wisc.edu/courses/st849-bates/lectures/Orthogonal.pdf
[9:3] Matrix decompositions for regression analysis §3.3 The Singular value decomposition D Bates – 2007 - http://www.stat.wisc.edu/courses/st849-bates/lectures/Orthogonal.pdf
[9:4] Gradient Descent for Linear Regression A. Ng Stanford - University Coursera NL lecture 9 - https://class.coursera.org/ml-003/lecture/9
[9:5] Stochastic gradient descent to find least square in linear regression - Qize Study and Research 2014 - http://qizeresearch.wordpress.com/2014/05/23/stochastic-gradient-descent-to-find-least-square-in-linear-regression/
[9:6] Apache Commons Math Library 3.3 §1.5 Multiple linear regression - The Apache Software Foundation - http://commons.apache.org/proper/commons-math/userguide/stat.html
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