CSCE 478/878 (Fall 2016) lecture slides
-
Lectures 0 and 1: Administrivia and Introduction, Aug 23–25, Chapters 1–2
(pdf6up)
Also see:
- Homework 0 due Aug 30
-
Lecture 2: Supervised Learning,
Aug 25–30, Chapter 2
(pdf1up,
pdf6up)
- Homework 1
due
Sunday, September 25
-
Mini-Homework 1.5
due
Thursday, September 15
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Lecture 4: Experimental Design and Analysis
Sep 8–15, Chapter 19 and ROC tutorial
(pdf1up,
pdf6up)
Also see:
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Lecture 5: Artificial Neural Networks and Support Vector Machines
Sep 20–Oct 6, Chapters 10, 11, and 13
(pdf1up,
pdf6up)
Also see:
-
Homework 2
due
Sunday, October 16
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Mini-Homework 2.5
due
Friday, October 7
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Lecture 6: Bayesian learning,
Oct 6–20, Chapter 14
(pdf1up, pdf6up)
Also see:
-
The Bayesian Trap.
Veritasium.
- Eugene Charniak. Bayesian
Networks without Tears. AI Magazine, vol. 12, no. 4 (1991).
- Nir Friedman and Daphne Koller. Learning Bayesian Networks from Data (NIPS tutorial).
- Pedro Domingos and Michael Pazzani.
Beyond
independence: Conditions for the optimality of the simple bayesian classifier.
In Proceedings of the 13th International Conference on Machine Learning,
1996.
- Stuart Russell
and Peter Norvig.
Artificial
Intelligence: A Modern Approach, Third Edition.
Prentice Hall,
2009.
- Richard E. Neapolitan.
Learning Bayesian Networks.
Prentice Hall,
2004.
- Neapolitan's KDD lecture on causality
- Russ Greiner's page of
links on Bayes nets
- Tom
Dietterich's course on Probabilistic Relational Models
- Geiger
et al.'s paper on algorithms for d-separation
- Judea Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of
Probabilistic Inference. Morgan Kauffmann, 1988.
- Daphne Koller, Nir Friedman, Lise Getoor, and Ben Taskar. Graphical Models in a Nutshell.
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Homework 3
due Sunday, October 30
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Mini-Homework 3.5
due Wednesday, October 26
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Lecture 7: Bagging and Boosting,
Oct 25–27, Chapter 17, Breiman paper, Freund & Schapire paper
(pdf1up, pdf6up)
Also see:
- Leo Breiman.
Bagging predictors.
Technical Report 421. Dept. of Statistics, University of California-Berkeley.
September 1994.
- Yoav Freund and
Robert Schapire.
A short
introduction to boosting. Journal of Japanese Society for Artificial
Intelligence 14(5):771–780, September, 1999. [Other introductory
and advanced papers are available at Freund's and Schapire's pages.]
-
Robert Schapire and Yoav Freund.
Boosting: Foundations and Algorithms.
MIT Press, 2012. ISBN 0-262-01718-0.
- Nick Littlestone and
Manfred K. Warmuth.
The weighted majority algorithm.
Information and Computation 108(2):212–261. February 1994.
Lecture 8: Clustering,
Oct 27–Nov 1, Chapter 7
(pdf1up,
pdf6up)
Lecture 9: Hidden Markov Models,
Nov 3–15, Chapter 15
(pdf1up,
pdf6up)
Also see:
-
Biological Sequence Analysis: Probabilistic Models of Proteins and
Nucleic Acids, by Richard Durbin, Sean Eddy, Anders Krogh, and Graeme
Mitchison. Cambridge University Press, 1998. ISBN 0-521-62971-3.
- Section 9.6 of Pattern
Recognition by Sergios Theodoridis and Konstantinos Koutroumbas
- K. Sjölander,
K. Karplus, M. Brown, R. Hughey, A. Krogh, I. S. Mian, and D. Haussler.
Dirichlet mixtures: A method for improving detection of weak but significant
protein sequence homology. Computer Applications in the Biosciences
(CABIOS), 12(4):327–345, 1996.
Mini-Homework 4.5
due Tuesday, November 15
Lecture 10: How to give a good research talk,
Nov 15–17
(pdf1up,
pdf6up)
Also see:
Lecture 11: Reinforcement learning, Nov 17–22
(pdf1up,
pdf6up)
Also see:
- Gerald Tesauro. Temporal Difference Learning and TD-Gammon.
Communcations of the ACM 38(3):58-68, March 1995.
- Richard Sutton and
Andrew Barto.
Reinforcement
Learning: An Introduction.
MIT Press, 1998.
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