Transcription of Introduction to Statistical Machine Learning
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Introduction to Statistical Machine Learning
Introduction to Statistical Machine Learning - 1 -Marcus HutterIntroduction toStatistical Machine LearningMarcus HutterCanberra, ACT, 0200, AustraliaMachine Learning Summer SchoolMLSS-2008, 2 15 March, KioloaANURSISENICTAI ntroduction to Statistical Machine Learning - 2 -Marcus HutterAbstractThis course provides a broad Introduction to the methods and practiceof Statistical Machine Learning , which is concerned with the developmentof algorithms and techniques that learn from observed data byconstructing stochastic models that can be used for making predictionsand decisions. Topics covered include Bayesian inference and maximumlikelihood modeling; regression , classification, density estimation,clustering, principal component analysis; parametric, semi-parametric,and non-parametric models; basis functions, neural networks, kernelmethods, and graphical models; deterministic and stochasticoptimization; overfitting, regularization, and to Statistical Machine Learning - 3 -Marcus HutterTable of / Overview / Methods for Methods for Assessment & & (Re)Active 4 -Marcus Hutter1 INTRO/OVERVIEW/PRELIMINARIES What is Machine Learning ?
Linear Methods for Regression - 21 - Marcus Hutter Coe–cient Shrinkage Solution 2: Shrinkage methods: Shrink the least squares w by penalizing the Loss: Ridge regression: Add / jjwjj2 2. Lasso: Add / jjwjj1. Bayesian linear regression: Comp. MAP argmaxw P(wjD) from prior P (w) and sampling model P (Djw). Weights of low variance components ...
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