CSCE 970 lecture slides
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Lectures 0 and 1: Administrivia and Introduction
(ps,
pdf)
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Lecture 2: Bayesian-Based Classifiers
(ps,
pdf)
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Lecture 3: Linear Classifiers
(ps,
pdf)
Also see:
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Lecture 4: Nonlinear Classifiers
(ps,
pdf)
Also see:
- ANN growing and pruning:
- SVMs:
- Decision trees:
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Lecture 5: Hidden Markov Models
(ps,
pdf)
Also see:
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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.
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Lecture 7: Clustering: Basic Concepts
(ps,
pdf)
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Lecture 8: Sequential Clustering Algorithms
(ps,
pdf)
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Lecture 9: Hierarchical Clustering Algorithms
(ps,
pdf)
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Lecture 10: Clustering Algorithms Based on Cost Function Optimization
(ps,
pdf)
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Special Lecture: How to Give a Good Research Talk
(ps,
pdf)
Also see:
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Lecture 11: Clustering Tendency and Cluster Validity
(ps,
pdf)
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