Filename
Size
Date
MIT machine learning course
...........................\Introduction.pdf
...........................\lagrange.pdf
...........................\lecture1- Introduction.pdf
...........................\lecture10-Boosting
 complexity.pdf
...........................\lecture11-Structural risk minimization
 description length.pdf
...........................\lecture12-Mixture models
 EM.pdf
...........................\lecture13-EM
 regularization
 conditional mixtures.pdf
...........................\lecture14-Non-parametric density estimation
 clustering.pdf
...........................\lecture15- Clustering
 Markov models.pdf
...........................\lecture16-Markov and hidden Markov models.pdf
...........................\lecture17-Hidden Markov models.pdf
...........................\lecture18-Viterbi
 graphical models.pdf
...........................\lecture19-Bayesian networks.pdf
...........................\lecture2-Linear regression.pdf
...........................\lecture20-Medical diagnosis example
 influence diagrams.pdf
...........................\lecture21-Influence diagrams
 exact inference.pdf
...........................\lecture22-Belief propagation.pdf
...........................\lecture23-Learning graphical models (guest lecture).pdf
...........................\lecture3- Additive models
 maximum likelihood.pdf
...........................\lecture3-Additive models
 maximum likelihood.pdf
...........................\lecture4-Active learning.pdf
...........................\lecture5-Classification.pdf
...........................\lecture6- Logistic regression
 regularization.pdf
...........................\lecture7-Regularization
 Support vector machines.pdf
...........................\lecture8-Support vector machines
 text classification.pdf
...........................\lecture9-Feature selection
 combination of methods.pdf
...........................\Linear regression.pdf
...........................\regularization.pdf