Machine Learning

Spring 2004, CMSC 726
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Related Links
ICML 2003
IEEE Explore
Weka
ACM SIGIR
ACM SIGKDD
Kurt Thearling's list of books
Beginner's guide to Matlab
Matlab Tutorial

Class Topics

Topic Slides Required Reading Optional Reading
Introduction ppt pdf Ch.1 from I2ML
Supervised Learning ppt pdf Ch.2 from I2ML
Linear Classifiers I ppt pdf Andrew Ng's notes:
Linear Classifiers II ppt pdf
Decision Trees ppt pdf Read any one of the following:
  • Ch. 9.1-9.4 from I2ML
  • Ch. 3 from ML
  • Ch. 8.2-8.4 from DHS
Non-Parametric Classifiers ppt pdf Read any one of the following:
  • Ch. 8 from I2ML
  • Ch. 4.4-4.6 from DHS
  • Ch. 4.4-4.6 from DHS
Evaluation ppt pdf
Support Vector Machines ppt pdf Read any one of the following:
Bias-Variance Tradeoff ppt pdf
Bayesian Learning ppt pdf
Neural Networks pdf
Clustering I ppt pdf Read material from any one of I2ML or HMS:
  • Sec. 9.6, 6.4, 7.3.2, 7.4 and 8.4 from HMS
  • Ch. 7 from I2ML
Clustering II ppt pdf Andrew Ng's notes: Optional reading:
Spectral Clustering ppt pdf
Bayes nets: Representation ppt pdf
Bayes nets: Inference ppt pdf Please read the following:
  • Chapter 7 from BNB
  • Sections 8.1,8.2,8.3 and 8.7 from BNB
Bayes nets: Learning ppt pdf Please read Chapter 15 from BNB Optional reading: Tutorial on Learning with Bayes nets
Hidden Markov Models ppt pdf HMM notes
Reinforcement Learning ppt pdf Read any one of:
Wrap up ppt pdf

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