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Jaakkola
Results: 79

#	Item
31	6.867 Machine learning, lecture 16 (Jaakkola) 1 Lecture topics: • Mixture of Gaussians (cont’d) Add to Reading List Source URL: ocw.mit.edu Language: English - Date: 2015-03-15 16:36:56 Statistical theory M-estimators Expectation–maximization algorithm Missing data Maximum likelihood Mixture model Conjugate prior Likelihood function Normal distribution Statistics Estimation theory Bayesian statistics
32	6.867 Machine learning, lecture 7 (Jaakkola) 1 Lecture topics: • Kernel form of linear regression Add to Reading List Source URL: ocw.mit.edu Language: English - Date: 2015-03-15 16:36:56 Operator theory Normal distribution Vector space Algebra Mathematics Vectors
33	6.867 Machine learning, lecture 10 (Jaakkola) 1 Lecture topics: model selection criteria • Structural risk minimization, example derivation Add to Reading List Source URL: ocw.mit.edu Language: English - Date: 2015-03-15 16:36:56 Bayesian statistics Econometrics Bayesian inference Maximum likelihood Loss function Likelihood function Marginal likelihood Normal distribution Bayesian information criterion Statistics Estimation theory Statistical theory
34	6.867 Machine learning, lecture 9 (Jaakkola) 1 Lecture topics: • Kernel optimization Add to Reading List Source URL: ocw.mit.edu Language: English - Date: 2015-03-15 16:36:56 Statistical classification Machine learning Support vector machine Kernel Regularization Statistics Algebra Mathematics
35	6.867 Machine learning, lecture 13 (Jaakkola) 1 Lecture topics: • Boosting, margin, and gradient descent Add to Reading List Source URL: ocw.mit.edu Language: English - Date: 2015-03-15 16:36:56 AdaBoost Boosting Perceptron Computer programming Ensemble learning Learning Statistics
36	6.867 Machine learning, lecture 14 (Jaakkola) 1 Lecture topics: • margin and generalization Add to Reading List Source URL: ocw.mit.edu Language: English - Date: 2015-03-15 16:36:56 Dimension Linear algebra Statistical classification Computational learning theory Mixture model VC dimension Kernel Mixture distribution Perceptron Algebra Mathematics Statistics
37	6.867 Machine learning, lecture 12 (Jaakkola) 1 Lecture topics: model selection criteria • Feature subset selection (cont’d) Add to Reading List Source URL: ocw.mit.edu Language: English - Date: 2015-03-15 16:36:56 Estimation theory Statistical theory Information theory Naive Bayes classifier Mutual information Likelihood function Loss function Statistics Bayesian statistics Statistical classification
38	6.867 Machine learning, lecture 1 (Jaakkola) 1 Example Let’s start with an example. Suppose we are charged with providing automated access Add to Reading List Source URL: ocw.mit.edu Language: English - Date: 2015-03-15 16:36:56 Statistical theory Perceptron Loss function Function Statistics Mathematics Neural networks
39	6.867 Machine learning, lecture 8 (Jaakkola) 1 Lecture topics: • Support vector machine and kernels Add to Reading List Source URL: ocw.mit.edu Language: English - Date: 2015-03-15 16:36:56 Support vector machine Vector space Lagrange multiplier Algebra Mathematics Statistical classification
40	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

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