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 IN
Dichotomies in Machine Learning scope of my lecture, scope of other lectures (machine) learning / statistical, logic/knowledge-based (GOFAI) induction, prediction, decision, action regression, classiflcation independent identically distributed, sequential / non-iid online learning, o†ine/batch learning passive prediction, active learning
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