Download
1 / 38
380 likes | 389 Views
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
E N D
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
