Outline

• Introduction
• Machine learning tools and techniques.
  • What's it all about?
    • Introduction
    • Input: Concepts, instances, and attributes
    • Output: Knowledge representation
• Mining Association Rules
• Classification
  • Evaluation Performance.
    • Training and Testing.
    • Cross-validation.
    • Predicting performance.
  • Decision Tree Learning 
  • Bayesian Decision Theory
    • Naive Bayes Classifier (From Tom M. Mitchell)
    • Naive_Bayes_training_example_Tennis.htm
      • Naive_Bayes_training_example_Tennis_ans.htm
  • Instance-based Learning
  • Linear Classifier.
    • LinearClassifier_jdwang.xls
  • Support Vector Machine (SVM)
• Dimensional Reduction
  • Clustering
  • Attribute Selection
• Text Mining
  • Biomedical Corpora
  • Text Mining Application Programming
    • Chapter1_Introduction
    • Chapter3_ExploringText
    • Chapter 9 Text Categorization
    • TextMining_StopWord_Stemming_example    StopWordStemmingZIP
      • ActivePerl
• Hidden Markov Model
• Neural Network
  • Introduction
  • Perceptron
  • Multilayer perceptrons
• Genetic Algorithm