Lectures 0 and 1: Administrivia and Introduction
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pdf)
Lecture 2: Bayesian-Based Classifiers
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pdf)
Lecture 3: Linear Classifiers
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pdf)
Also see:
- Manfred K. Warmuth, who has done much work on EG and Winnow (many papers available on-line, including GD/EG paper)
- Nick Littlestone (creator of Winnow) and some of his papers:
- N. Littlestone. ``Learning Quickly When Irrelevant Attributes Abound: A New Linear-threshold Algorithm''. Machine Learning, 2:285-318, 1988.
- N. Littlestone. ``Redundant noisy attributes, attribute errors, and linear threshold learning using Winnow''. In Proc. 4th Annu. Workshop on Comput. Learning Theory, 147-156, 1991. Morgan Kaufmann.
- Avrim Blum and his survey paper ``On-Line Algorithms in Machine Learning''
- Thomas G. Dietterich and his paper ``Solving Multiclass Learning Problems via Error-Correcting Output Codes''
Lecture 4: Nonlinear Classifiers
(ps,
pdf)
Also see:
- ANN growing and pruning:
- Christopher M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, 1995. (Section 9.5)
- SVMs:
- Nello Cristianini and John Shawe-Taylor. An Introduction to Support Vector Machines. Cambridge University Press, 2000.
- Richard Duda, Peter Hart, and David Stork. Pattern Classification, 2nd Edition. [also see software supplements] John Wiley, 2001. (Section 5.11)
- SVM tutorial
- KernelMachines.org
- Decision trees:
- Tom Mitchell. Machine Learning. McGraw-Hill, 1997. (Chapter 3)
Lecture 5: Hidden Markov Models
(ps,
pdf)
Also see:
- R. Durbin, S. Eddy, A. Krogh, and G. Mitchison. Biological Sequence Analysis. Cambridge University Press, 1998. [see ch. 3]
- Ron Shamir's course on computational biology [see the scribe notes on hidden Markov models]
- ISMB99 Tutorial on HMMs
- K. Sjolander et al. Dirichlet mixtures: A method for improving detection of weak but significant protein sequence homology. Technical Report UCSC-CRL-96-09, University of California in Santa Cruz, 1996.
- Source code (specific to biological sequence analysis):
- SAM (Sequence Alignment and Modeling System)
- HMMER [Profile hidden Markov models for biological sequence analysis]
Lecture 6: System Evaluation and Combining Classifiers
(ps,
pdf)
Also see:
- Tom Mitchell. Machine Learning. McGraw-Hill, 1997. (Chapters 5 and 7)
- Avrim Blum and his survey paper ``On-Line Algorithms in Machine Learning''
- Leo Breiman. ``Bagging Predictors.'' Technical report 421, Department of Statistics, University of California, Berkeley. September 1994.
- Yoav Freund and Robert Schapire. ``A Short Introduction to Boosting.'' Journal of the Japanese Society for Artificial Intelligence, 14(5):771-780. September 1999.
- Thomas G. Dietterich. ``Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms.'' Neural Computation, 10(7):1895-1924. 1998.
Lecture 7: Clustering: Basic Concepts
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Lecture 8: Sequential Clustering Algorithms
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Lecture 9: Hierarchical Clustering Algorithms
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Lecture 10: Clustering Algorithms Based on Cost Function Optimization
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Special Lecture: How to Give a Good Research Talk
(ps,
pdf) Also see:
Lecture 11: Clustering Tendency and Cluster Validity
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