Machine Learning

jdwang@asia.edu.tw, Room:5317, ext:1847

• Text Book

  • Introduction to Machine Learning
    by Ethem Alpaydin

  • http://www.cmpe.boun.edu.tr/~ethem/i2ml/

  Hardcover: 400 pages

  Publisher: The MIT Press (October 1, 2004)

  ISBN: 0262012111

• Reference Book

  • Machine Learning, Tom Mitchell, McGraw Hill, 1997.ISBN 0070428077

    • Machine Learning, Tom Mitchell Slides for instructors
    • http://www.nada.kth.se/~hoffmann/ml.html

  Hardcover: 432 pages

  Publisher: McGraw-Hill Science/Engineering/Math (March 1, 1997)

  ISBN: 0070428077

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• Outline
  • (2006.3.2) Introduction (Chapter1)
    • What is Machine Learning ?
      • Machine learning is programming computers to optimize a performance criterion using example data or past experience.
      • Machine learning uses the theory of statistics in building mathematical  models, because the core task is making inference from a sample.
      • Machine learning is not just a database problem; it is also a part of artificial intelligence.
    • data mining :
      • Application of machine learning methods to large databases.
  • (2006.3.2) Supervised Learning (Chapter 2)

    • Overfitting
    • Cross-validation

  • Bayesian Decision Theory (Chapter 3)
    • Naive Bayes Classifier (From Tom M. Mitchell
      Center for Automated Learning and Discovery
      Carnegie Mellon University)
  • Parametric Methods (Chapter 4)
  • Dimensional Reduction (Chapter 6)
  • Decision Tree Learning (Chapter 9)
  • Instance-based Learning

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• Grading (成績)
  • Project Group Member List
    • (2006.3.30)
  • Middle Exam(50%)
    • (2006.4.20 3:10pm~4:30pm)
    • Hot Spots
  • Project (25%)   
    • (2006.5.18)
    • DNA Sequence Classification
    • 個別貢獻度
    • 工作內容
  • Final Report (25%)
    • How can you solve the problem by machine-learning approach?
    • (2006.5.25 before 11:30am)