CSCE 970 lecture slides

Entries in red do not have slides updated for spring 2003. Refer to the the spring 2001 offering for old copies of slides.
o Lectures 0 and 1: Administrivia and Introduction, Jan 14. Theodoridis Chapter 1. (ps, pdf)
Topics: Features (attributes) and feature vectors, classification, supervised vs. unsupervised learning

o Lecture 2: Bayesian-Based Classifiers, Jan 16-21. Theodoridis Sections 2.1-2.4, 2.5.1, 2.5.2, 2.5.6, 2.6. (ps, pdf) Topic summary 1 due Thursday, Feb 6
Topics: Bayesian decision theory, discriminant functions, Bayesian classification for Gaussian distributions, estimation of unknown pdfs, k-nearest neighbor techniques

o Lecture 3: Linear Classifiers, Jan 21-28. Theodoridis Sections 3.1-3.3, 3.4.1, 3.4.2, 3.5 (skim), pages 1-19 of GD/EG paper. (ps, pdf) Topic summary 2 due Tuesday, Feb 11
Topics: Linear discriminant functions, perceptron algorithm, Winnow, exponentiated gradient, least squares methods
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o Lecture 4: Nonlinear Classifiers. Theodoridis Sections 4.1-4.4, 4.6 (skip proof), 4.7, 4.9, 4.10, 4.13-4.15, 4.17. (ps, pdf) Topic summary 3 due Tuesday, Mar 11
Topics: 2- and 3-layer perceptrons, backpropagation, setting network size (especially pruning), Cover's theorem, RBF networks, decision trees
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o Lecture 5: Hidden Markov Models. Durbin Chapter 3, Theodoridis Sections 9.1-9.4, 9.6, 9.8. (ps, pdf) Topic summary 4 due Thursday, March 27
Topics: Markov models, the Viterbi algorithm, hidden Markov models, Baum-Welch algorithm.
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o Lecture 6: System Evaluation and Combining Classifiers. Theodoridis Chapter 10, selected papers. (ps, pdf) Topic summary 5 due Tuesday, April 8
Topics: Estimating classification error (confidence intervals, paired t tests, cross-validation), improving performance (bagging, boosting, weighted majority).
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o Lecture 7: Clustering: Basic Concepts. Theodoridis Chapter 11, Sections 12.1-12.2. (ps, pdf)
Topics: Applications, examples, cluster types, feature types, proximity measures, categories of algorithms.

o Lecture 8: Sequential Clustering Algorithms. Theodoridis Sections 12.3-12.6. (ps, pdf)
Topics: BSAS, MBSAS, TTSAS, estimating the number of clusters.

o Lecture 9: Hierarchical Clustering Algorithms. Theodoridis Sections 13.1, 13.2.1-13.2.4, 13.5. (ps, pdf) Topic summary 6 (on Lectures 7-9) due Tuesday, April 15
Topics: Agglomerative schemes (dendograms, single link algorithm, complete link algorithm), determining the best number of clusters.

o Lecture 10: Clustering Algorithms Based on Cost Function Optimization. Theodoridis Sections 14.1, 14.3.1, 14.3.6, 14.5, selected papers. (ps, pdf) Topic summary 7 due Tuesday, April 22
Topics: Isodata algorithm, fuzzy clustering methods (also fuzzy classification, if time permits).

o Lecture 11: Clustering Tendency and Cluster Validity. Theodoridis Chapter 16. (ps, pdf NOTE: These are different from what was handed out in class; two more slides are in this set.)
Topics: Hypothesis testing, internal criteria, external criteria, relative criteria, validity of individual clusters, cluster tendency.
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o Special Lecture: How to Give a Good Research Talk. (ps, pdf)
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