CSCE 478/878 (Fall 2010) lecture slides

• Lectures 0 and 1: Administrivia and Introduction, Aug 23–25, Chapter 1 (pdf6up)
  Also see:
  • Andrew Ng's Lecture 1 of his course at Stanford
  • Andrew Ng's Lecture 2 of his course at Stanford (discusses autonomous vehicle)


• Lecture 2: Concept Learning and the General-to-Specific Ordering, Aug 25–Sep 3, Chapter 2 (pdf6up)


• Lecture 3: Decision Trees, overfitting, Occam's razor, Sep 3–15, Chapter 3 (pdf6up)
  Also see:
  • Manuals/tutorials for C4.5


• Labor Day Sep 6; no class


• Lecture 4: Artificial Neural Networks and Support Vector Machines Sep 24–Oct 13, Chapter 4, Section 7.4.4, Littlestone paper, handout from Christianini and Shawe-Taylor's book (pdf6up)
  Also see:
  • Manfred K. Warmuth, who has done much work on EG and Winnow. Many papers available on-line, including his GD/EG paper:
    • Jyrki Kivinen and Manfred K. Warmuth. Exponentiated gradient versus gradient descent for linear predictors. Information and Computation 132(1):1–64, January 1997.
  • Nick Littlestone (creator of Winnow) and some of his papers:
    • Nick Littlestone. Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm. Machine Learning 2:285–318, 1988. [The linked paper is not the final version, but a similar technical report.]
    • Nick 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''
  • Notes from CSCE 970 on nonlinear classifiers [on the intuition behind using multiple layers to remap the input space] (pdf4up)
  • ANN growing and pruning:
    • Christopher M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, 1995. (Section 9.5)
  • SVMs:
    • Muller, K.-R., Mika, S., Ratsch, G., Tsuda, K., and Scholkopf, B. An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks, 12(2):181-201, 2001.
    • 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. John Wiley, 2001. (Section 5.11)
    • SVM tutorial
    • KernelMachines.org
    • Sebastian Thrun's links


• Lecture 5: Evaluating Hypotheses Oct 6–Oct 20, Chapter 5 and ROC tutorial (pdf6up)
  Also see:
  • ICML 04 ROC tutorial by Peter Flach
  • Thomas G. Dietterich. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation 10(7):1895–1924.
  • Chris Drummond and Robert C. Holte Cost curves: An improved method for visualizing classifier performance. Machine Learning, 65(1):95–130, 2006.


• Fall break, Oct 18, no class


• Lecture 6: Bayesian learning, Oct 20–Nov 5, Chapter 6 (pdf6up)
  Also see:
  • 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.


• Lecture 7: Instance-based learning, Nov 8–15, Chapter 8 (pdf6up)
  Also see:
  • Case-based reasoning (thanks to Brandon Hauff):
  • Aamodt, A. and Plaza, E. (1994). Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI Communications, 7(i): pp 39–59.
  • CBR tutorial
  • David Aha, ed. Lazy Learning. Kluwer Academic Publishers, 1997.
  • Christopher M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, 1995. [RBF networks]
  • Sergios Theodoridis and Konstantinos Koutroumbas. Pattern Recognition. [distance measures between instances]


• Lecture 8.5: How to give a good research talk, Nov 17–19 (pdf6up)
  Also see:
  • Tips on Presenting Technical Material

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