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Statistical learning theory
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#	Item
431	M ANIFOLD R EGULARIZATION Francesca Odone and Lorenzo Rosasco RegML 2014 Regularization Methods for High Dimensional Learning Add to Reading List Source URL: lcsl.mit.edu Language: English - Date: 2014-06-30 18:30:12 Physics Summability methods Statistical classification Mathematics Supervised learning Regularization Manifold Support vector machine Solid modeling Machine learning Quantum field theory Mathematical analysis
432	Algorithmic Probability—Theory and Applications Ray J. Solomonoff Visiting Professor, Computer Learning Research Centre Royal Holloway, University of London IDSIA, Galleria 2, CH–6928 Manno–Lugano, Switzerland Add to Reading List Source URL: world.std.com Language: English - Date: 2014-07-03 19:23:22 Science Statistical inference Knowledge Ray Solomonoff Probability interpretations Epistemology Inductive inference Prior probability Algorithmic probability Statistics Artificial intelligence Bayesian statistics
433	6.867 Machine learning, lecture 11 (Jaakkola) 1 Lecture topics: model selection criteria • Minimum description length (MDL) Add to Reading List Source URL: ocw.mit.edu Language: English - Date: 2015-03-15 16:36:56 Statistical theory M-estimators Bayesian statistics Information Maximum likelihood Mutual information Likelihood function Minimum description length Conditional entropy Estimation theory Information theory Statistics
434	Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning, Springer Series in Statistics, 2001, pp. xvi+533. This is a great book. All three authors have track records for clear expositi Add to Reading List Source URL: statweb.stanford.edu Language: English - Date: 2004-03-05 12:46:46 Statistical theory Design of experiments Scientific method Quality Statistical methods Wavelet Algorithm Probability Complexity Science Statistics Information
435	JMLR: Workshop and Conference Proceedings vol:1–36, 2012 25th Annual Conference on Learning Theory A Method of Moments for Mixture Models and Hidden Markov Models Add to Reading List Source URL: newport.eecs.uci.edu Language: English - Date: 2012-05-22 10:47:20 Markov models Cluster analysis Machine learning Mixture model Expectation–maximization algorithm Hidden Markov model Maximum likelihood Matrix multiplication Independent component analysis Statistics Estimation theory Statistical models
436	A Bayesian Nonparametric Approach to Clustering Data from Underwater Robotic Surveys Daniel M. Steinberg, Ariell Friedman, Oscar Pizarro and Stefan B. Williams Abstract The use of robots for scientific mapping and explo Add to Reading List Source URL: www.isrr-2011.org Language: English - Date: 2011-08-18 11:40:14 Statistical models Statistical theory Cluster analysis Expectation–maximization algorithm Variational Bayesian methods Dirichlet process Mixture model Parametric model Bayesian inference Statistics Bayesian statistics Machine learning
437	Marking criteria Section: Experimental Research Division: Intermediate (9 – 10) VELS: Level 5, progressing to level 6 Student name: ………………………………………………….. Links to VELS progressio Add to Reading List Source URL: www.sciencevictoria.com.au Language: English - Date: 2008-02-18 00:02:42 Systems engineering Industrial engineering Psychometrics Victorian Essential Learning Standards Statistical hypothesis testing Science Research Evaluation Statistics Design of experiments Statistical theory
438	Marking criteria Section: Experimental Research Division: Junior (7 – 8) VELS: Level 4, progressing to level 5 Student name: ………………………………………………….. Links to VELS progression point Add to Reading List Source URL: www.sciencevictoria.com.au Language: English - Date: 2008-02-18 00:02:47 Systems engineering Victorian Essential Learning Standards Philosophy of science Psychometrics Statistical theory Validity Design of experiments Statistics Science Engineering statistics
439	Marking criteria Section: Experimental Research Division: Intermediate (9 – 10) Add to Reading List Source URL: www.sciencevictoria.com.au Language: English - Date: 2008-02-18 00:02:44 Systems engineering Industrial engineering Psychometrics Victorian Essential Learning Standards Statistical hypothesis testing Science Research Evaluation Statistics Design of experiments Statistical theory
440	1 Learning Graphical Model Parameters with Approximate Marginal Inference Justin Domke, NICTA & Australia National University Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2013-09-04 21:04:45 Expectation–maximization algorithm Maximum likelihood Bayes estimator Loss function Sufficient statistic Belief propagation Statistics Estimation theory Statistical theory

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