6.867 Machine Learning
6.867 Machine Learning
Fall 2002
Lectures
Lectures are Tue/Thu 2:30-4pm in E37-212
Lecture information and slides:
Lecture 1
(September 5) Introduction
pdf slides
or
6 per page (postscript)
Lecture 2
(September 10) Linear regression
(Jordan & Bishop: chapters 5-5.3)
pdf slides
or
6 per page (postscript)
Lecture 3
(September 12) Additive models, maximum likelihood
(Jordan & Bishop: chapter 4 (up to eq 4.20); chapters 5-5.3 & 5.6)
pdf slides
or
6 per page (postscript)
Lecture 4
(September 17) Active learning
pdf slides
or
6 per page (postscript)
Lecture 5
(September 19) Classification
(Jordan & Bishop: chapter 4 mixture models; chapter 6-6.3.1)
pdf slides
or
6 per page (postscript)
Lecture 6
(September 24) Logistic regression, regularization
pdf slides
or
6 per page (postscript)
Additional notes on regularization (
pdf file
)
Lecture 7
(September 26) Regularization, Support vector machines
pdf slides
or
6 per page (postscript)
Tutorial on Lagrange multipliers (
postscript
)
Optional paper on support vector machines (
postscript
)
Lecture 8
(October 1) Support vector machines, text classification
pdf slides
or
6 per page (postscript)
Lecture 9
(October 3) Feature selection, combination of methods
pdf slides
or
6 per page (postscript)
Optional papers:
boosting tutorial (postscript)
and
statistical view of boosting (postscript)
Lecture 10
(October 8) Boosting, complexity
pdf slides
or
6 per page (postscript)
Lecture 11
(October 10) Structural risk minimization, description length (+ midterm discussion)
pdf slides
or
6 per page (postscript)
MIDTERM
(October 17) (in class)
Lecture 12
(October 22) Mixture models, EM
(Jordan & Bishop: chapter 4 statistical concepts, mixture models; chapter 9 up to 9.2)
pdf slides
or
6 per page (postscript)
Lecture 13
(October 24) EM, regularization, conditional mixtures
(Jordan & Bishop: chapter 9 up to 9.2.3)
pdf slides
or
6 per page (postscript)
Lecture 14
(October 29) Non-parametric density estimation, clustering
pdf slides
or
6 per page (postscript)
Lecture 15
(October 31) Clustering, Markov models
pdf slides
or
6 per page (postscript)
Lecture 16
(November 5) Markov and hidden Markov models
(Jordan & Bishop: chapter 11)
pdf slides
or
6 per page (postscript)
Optional tutorial on HMM (and some applications in speech recognition) (
pdf
)
Lecture 17
(November 7) Hidden Markov models
pdf slides
or
6 per page (postscript)
Lecture 18
(November 12) Viterbi, graphical models
pdf slides
or
6 per page (postscript)
Lecture 19
(November 14)Bayesian networks
pdf slides
or
6 per page (postscript)
Lecture 20
(November 19)Medical diagnosis example, influence diagrams
pdf slides
or
6 per page (postscript)
Lecture 21
(November 21)Influence diagrams, exact inference
pdf slides
or
6 per page (postscript)
Lecture 22
(November 26) Belief propagation
pdf slides
or
6 per page (postscript)
Lecture 23
(December 3) Learning graphical models (guest lecture)
pdf slides
or
6 per page (postscript)
FINAL
(December 5) (in class)
Lecture 24
(December 10) no lecture, check email