Machine Learning
Introduction to Bayesian Learning
MMaacchhiinneeLeLaernainrgning FFaallll22020317
1
The slides are partly from Vivek Srikumar
What we have seen so far
What does it mean to learn?
? Mistake-driven learning
? Learning by counting (and bounding) number of mistakes
? PAC learnability
? Sample complexity and bounds on errors on unseen examples
Various learning algorithms
? Analyzed algorithms under these models of learnability ? In all cases, the algorithm outputs a function that produces
a label y for a given input x
2
Coming up
Another way of thinking about "What does it mean to learn?"
? Bayesian learning
Different learning algorithms in this regime
? Logistic Regression ? Na?ve Bayes
3
Today's lecture
? Bayesian Learning
? Maximum a posteriori (MAP) and maximum likelihood (ML) estimation
? Two examples of maximum likelihood (ML) estimation
? Binomial distribution ? Normal distribution
4
Today's lecture
? Bayesian Learning
? Maximum a posteriori (MAP) and maximum likelihood (ML) estimation
? Two examples of maximum likelihood estimation
? Binomial distribution ? Normal distribution
5
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