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Learn about Naive Bayes classifiers and perceptron learning algorithm in artificial intelligence. Understand how they work and how to apply them in machine learning tasks. Explore conditional probability tables and backpropagation.
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CS 4100 Artificial Intelligence Prof. C. Hafner Class Notes April 3, 2012
Term Project Presentations Thursday, April 12 Groups: 1. 2. 3. 4. Tuesday, April 17 Groups: 5. 6. 7. 8. 9.
Naive Bayes Classifiers: Our next example of machine learning • A supervised learning method • Making independence assumption, we can explore a simple subset of Bayesian nets, such that: • It is easy to estimate the CPT’s from sample data • Uses a technique called “maximum likelihood estimation” • Given a set of correctly classified representative examples • Q: What estimates of conditional probabilities maximize the likelihood of the data that was observed? • A: The estimates that reflect the sample proportions
# Juniors were Juniors and # Juniors were Non-Juniors # Non-Juniors
Class Exercise: Naive Bayes Classifier with multi-valued variables Major: Science, Arts, Social Science Student characteristics: Gender (M,F), Race/Ethnicity (W, B, H, A) International (T/F) What do the conditional probability tables look like??
Perceptron Learning (Supervised) • Assign random weights (or set all to 0) • Cycle through input data until change < target • Let α be the “learning coefficient” • For each input: • If perceptron gives correct answer, do nothing • If perceptron says yes when answer should be no, decrease the weights on all units that “fired” by α • If perceptron says no when answer should be yes, increase the weights on all units that “fired” by α
