Machine Learning

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

Time: Thursday, 6:30pm ~9:05pm, Room:5216

Outline http://asia.edu.tw/~jdwang/TeachingCourses/MachineLearning/2006_1_outline.htm

• Text Book

• Data Mining: Practical Machine Learning Tools and Techniques (2nd Edition), 天瓏代理

• Ian H. Witten & Eibe Frank. Morgan Kaufmann, June 2005,  ISBN 0-12-088407-0

• http://www.cs.waikato.ac.nz/ml/weka/book.html

• The Weka machine learning workbench.

• Reference Book

  • Introduction to Machine Learning, by Ethem Alpaydin  The MIT Press (October 1, 2004) ISBN: 0262012111

  • Bioinformatics : The Machine Learning Approach, 2nd Edition. The MIT Press (2001) ISBN: 026202506X

  • Machine Learning, Tom Mitchell, McGraw-Hill  (March 1, 1997) ISBN: 0070428077

  • Data Mining : A Tutorial-based Primer, Addison Wesley, 2003, ISBN : 0-201-74128-8

  • Text Mining Application Programming, Thomson, Manu Konchady, 2006, ISBN: 1-58450-460-9

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• Grading 成績
  • Project (50%): Decision Tree and Mining Association Rules
    • Please design a classifier using decision tree via Weka.
      • key points
        • feature selection (filter)
        • Converting discrete to numeric attributes.
        • accuracy comparison according to different decision trees.
    • Using the package Apriori  in Weka. to mining for association rules.
    • Make a report described what you have observed.
    • Make a discussion based on your experiments.
  • Project (50%): Linear Classifier vs SVM Classifier
    • DNA Sequence Classification
    • SVM Classifier
    • Presentation Date :
    • Report Date :