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

[9:7] Lecture 2: From linear Regression to Kalman Filter and Beyond S. Sarkka - Dept. of Biomedical Engineering and Computational Science, Helsinki University of Technology 2009 - http://www.lce.hut.fi/~ssarkka/course_k2009/slides_2.pdf

[9:8] Introductory Workshop on Time Series Analysis S McLaughlin - Mitchell Dept. of Political Science University of Iowa 2013 -http://qipsr.as.uky.edu/sites/default/files/mitchelltimeserieslecture102013.pdf

[9:9] Pattern Recognition and Machine Learning §3.1 Linear Basis Function Models C. Bishop - Springer 2006

[9:10] Machine Learning: A Probabilistic Perspective §13.1 L1 Regularization basics - K Murphy - MIT Press 2012

[9:11] Feature selection, L1 vs. L2 regularization, and rotational invariance A. Ng, - Computer Science Dept. Stanford University -http://www.machinelearning.org/proceedings/icml2004/papers/354.pdf

[9:12] Lecture 5: Model selection and assessment H. Bravo, R. Irizarry - Dept. of Computer Science, University of Maryland 2010 -http://www.cbcb.umd.edu/~hcorrada/PracticalML/pdf/lectures/selection.pdf

[9:13] Machine learning: a probabilistic perspective §9.3 Generalized linear models - K Murphy - MIT Press 2012

[9:14] An Introduction to Logistic and Probit Regression Models C. Moore - University of Texas 2013 -http://www.utexas.edu/cola/centers/prc/_files/cs/Fall2013_Moore_Logistic_Probit_Regression.pdf