CS 4100 Artificial Intelligence Prof. C. Hafner Class Notes April 3, 2012

Term Project Presentations Thursday, April 12 Groups: Tuesday, April 17 Groups:

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 # Non-Juniors were Juniors and were 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 Leaning Algorithm and BackProp

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