CS579-1 Machine Learning Spring 2005
Lecture Slides and Handouts

• January 13: What is Machine Learning?
  On CS domain the file is located at ~ipivkina/cs579/Jan13.pdf
• January 20: Apriori association rule learner.
  On CS domain the file is located at ~ipivkina/cs579/Jan20Apriori.pdf
• February 3: naive Bayes Classifier.
  On CS domain the file is located at ~ipivkina/cs579/Feb3naiveBayes.pdf
• February 8:
  • Slides by Gregory Piatetsky-Shapiro on basic methods for classification
    (includes 1R and Naive Bayes with Laplace estimator)
    On CS domain the file is located at ~ipivkina/cs579/basicClassifiers.pdf
  • Least Mean Squares (checkers example)
    On CS domain the file is located at ~ipivkina/cs579/Feb8LMS.pdf
• February 17: Probability Densities.
  Slides by Andrew Moore (Carnegie Mellon University)
  Also available at ~ipivkina/cs579/Feb17probDensity.pdf
• February 22: Nearest neighbor.
  On CS domain the file is located at ~ipivkina/cs579/Feb22nearestn.pdf
• March 3: Support Vector machines.
  On CS domain the file is located at ~ipivkina/cs579/Mar3svm.pdf
• March 29, 31: Neural Networks.
  On CS domain the file is located at ~ipivkina/cs579/NeuralNet.pdf
• April 5: Clustering and You by Evan Clark.
• April 7: Decision Tree Methods by Mayur Palankar.
• April 12: Anomaly Detection by Brian Palmer.
• April 13: Neural Network Approach to Solving the Traveling Salesman Problem by Adam Noonchester.
• April 14: Predictioin of Protein Secondary Structure using Neural Networks by Kalyan Gopavarapu.
• April 19: Genetic Algorithms by Senlin Liang.
• April 20: Inductive Logic Programming by Brad Morgan.
• April 21: Language Acquisition with Recurrent Neural Networks by Michael Dandini.
• May 3: Neural Network Camping by Vitus Lorenz-Meyer.
• May 5: FacePrints, Maze Solver and Genetic algorithms by Jacob Blumberg.