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Matlab
6.867 Lectures

## Lectures

Mon/Wed 2:30-4pm in 35-225

Lecture material:

• Lecture 1 (Wed 9/3): Introduction
pdf slides, 6 per page postscript

• Lecture 2 (Mon 9/8): Linear regression, estimation/estimator, generalization
(Jordan & Bishop: chapters 5-5.3)
pdf slides, 6 per page postscript

• Lecture 3 (Wed 9/10): Additive models, statistical regression models, maximum likelihood estimation
(Jordan & Bishop: chapter 4 (up to eq 4.20); chapters 5-5.3 & 5.6)
pdf slides, 6 per page postscript

• Lecture 4 (Mon 9/15): Bias/variance of estimators, active regression, examples
(no relevant reading in the book)
pdf slides, 6 per page postscript

• Lecture 5 (Wed 9/17): classification, discriminant, mixture models
(Jordan & Bishop: chapter 4 mixture models; chapter 6-6.3.1)
pdf slides, 6 per page postscript

• Lecture 6 (Wed 9/24): logistic regression, gradient ascent, back propagation, regularization
(additional notes on regularization)
pdf slides, 6 per page postscript

• Lecture 7 (Mon 9/29): regularization, regularized logistic regression, support vector machine
pdf slides, 6 per page postscript

• Lecture 8 (Wed 10/1): support vector machines, solution, kernels
(Additional notes on largrange multipliers, optional paper: postscript)
pdf slides, 6 per page postscript

• Lecture 9 (Mon 10/6): text classification, generative models, feature selection
pdf slides, 6 per page postscript

• Lecture 10 (Wed 10/8): feature selection, combination of methods, boosting + midterm discussion
pdf slides, 6 per page postscript

• MIDTERM (Wed 10/15)

• Lecture 11 (Mon 10/20): boosting, algorithm, performance
(optional papers: brief tutorial postscript, statistical view postscript)
pdf slides, 6 per page postscript

• Lecture 12 (Wed 10/22): complexity, VC-dimension, model selection
pdf slides, 6 per page postscript

• Lecture 13 (Mon 10/27): compression and model selection, probabilistic models
(Jordan & Bishop: chapter 4 statistical concepts, mixture models; chapter 9 up to 9.2)
pdf slides, 6 per page postscript

• Lecture 14 (Wed 10/29): Mixture models, EM, regularization
(Jordan & Bishop: chapter 4 statistical concepts, mixture models; chapter 9 up to 9.2)
pdf slides, 6 per page postscript

• Lecture 15 (Mon 11/3):
Mixtures and conditional mixtures
(Jordan & Bishop: chapter 9)
pdf slides, 6 per page postscript

• No lecture (Wed 11/5)

• Lecture 16 (Med 11/12):
Clustering, k-means, spectral clustering
pdf slides, 6 per page postscript

• Lecture 17 (Mon 11/17):
Semi-supervised clustering, clustering by dynamics, Markov and hidden Markov models.
pdf slides, 6 per page postscript

• Lecture 18 (Wed 11/19):
Hidden Markov models, forward-backward, EM
pdf slides, 6 per page postscript

• Lecture 19 (Mon 11/24):
Viterbi, alignment, graphical models
pdf slides, 6 per page postscript

• Lecture 20 (Wed 11/26):
Bayesian networks
pdf slides, 6 per page postscript

• Lecture 21 (Mon 12/1):
Bayesian networks, quantitative probabilistic inference
pdf slides, 6 per page postscript

• Lecture 22 (Wed 12/3):
Exact inference, junction trees
pdf slides, 6 per page postscript

• Lecture 23 (Mon 12/8):
Exact inference (briefly), approximate inference, and review for the final
pdf slides, 6 per page postscript

• FINAL EXAM (Wed 12/10)