CS 4100 Artificial Intelligence

Prof. C. HafnerClass Notes April 3, 2012

Term Project PresentationsThursday, 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

# Juniors

# Non-Juniors

were Juniors and

were Non-Juniors

Class Exercise: Naive Bayes Classifier with multi-valued variables

Major: Science, Arts, Social ScienceStudent 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