Files

MIT Machine Learning Lectures

Name		Name	Last commit message	Last commit date
parent directory ..
lecture-1-introduction.pdf		lecture-1-introduction.pdf
lecture-10-boosting.pdf		lecture-10-boosting.pdf
lecture-11-complexity.pdf		lecture-11-complexity.pdf
lecture-12-risk minimization.pdf		lecture-12-risk minimization.pdf
lecture-13-mixtures.pdf		lecture-13-mixtures.pdf
lecture-14-em algorithm.pdf		lecture-14-em algorithm.pdf
lecture-15-mixture classifiers.pdf		lecture-15-mixture classifiers.pdf
lecture-16-clustering.pdf		lecture-16-clustering.pdf
lecture-17-semi-supervised.pdf		lecture-17-semi-supervised.pdf
lecture-18-hmm.pdf		lecture-18-hmm.pdf
lecture-19-hmm2.pdf		lecture-19-hmm2.pdf
lecture-2-linear regression.pdf		lecture-2-linear regression.pdf
lecture-20-bayesian networks.pdf		lecture-20-bayesian networks.pdf
lecture-21-markov random field.pdf		lecture-21-markov random field.pdf
lecture-22-messgae passing.pdf		lecture-22-messgae passing.pdf
lecture-23-learning bayesian nets.pdf		lecture-23-learning bayesian nets.pdf
lecture-24-learning bayesian nets2.pdf		lecture-24-learning bayesian nets2.pdf
lecture-3-additive regression.pdf		lecture-3-additive regression.pdf
lecture-4-regression model.pdf		lecture-4-regression model.pdf
lecture-5-logistic.pdf		lecture-5-logistic.pdf
lecture-6-logistic 2.pdf		lecture-6-logistic 2.pdf
lecture-7-kernel methods.pdf		lecture-7-kernel methods.pdf
lecture-8-kernel methods 2.pdf		lecture-8-kernel methods 2.pdf
lecture-9-feature selection.pdf		lecture-9-feature selection.pdf