Bayesian Learning

This talk is based on

• Tom M. Mitchell. Machine Learning. McGraw Hill. 1997. Chapter 6.
• and his slides.

1. 1 Introduction
2. 2 Bayes Theorem
   1. 2.1 Choosing a Hypothesis
   2. 2.2 Example
   3. 2.3 Probability Formulas
3. 3 Brute Force Bayes Concept Learning
   1. 3.1 Relation to Concept Learning
   2. 3.2 MAP Hypothesis and Consistent Learners
4. 4 Learning A Real-Valued Function
5. 5 Learning To Predict Probabilities
6. 6 Minimum Description Length Principle
7. 7 Bayes Optimal Classifier*
   1. 7.1 Bayes Optimal Classification
8. 8 Gibbs Algorithm
9. 9 Naive Bayes Classifier
   1. 9.1 Naive Bayes Classifier
   2. 9.2 Naive Bayes Algorithm
   3. 9.3 Naive Bayes Example
   4. 9.4 Naive Bayes Issues
10. 10 Learning to Classify Text
    1. 10.1 Text Attributes
    2. 10.2 Learn Naive Bayes Text
    3. 10.3 Classify Naive Bayes Text
11. 11 Bayesian Belief Networks
    1. 11.1 Conditional Independence
    2. 11.2 Bayesian Belief Network
    3. 11.3 Inference in Bayesian Networks
    4. 11.4 Learning Bayesian Networks
       1. 11.4.1 Learning Bayesian Networks
       2. 11.4.2 Gradient Ascent for Bayes Nets
    5. 11.5 The Expectation Maximization Algorithm
       1. 11.5.1 When To Use EM Algorithm
       2. 11.5.2 EM Example: Generating Data from k Gaussians
       3. 11.5.3 EM for Estimating k Means
       4. 11.5.4 EM Algorithm
       5. 11.5.5 General EM Problem
       6. 11.5.6 General EM Method
    6. 11.6 Summary of Bayes Nets

Entire Presentation with Notes

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Copyright © 2009 José M. Vidal . All rights reserved.

25 February 2003, 02:16PM