Midterm Review
The Midterm
• Everything we have talked about so far
• Stuff from HW
• I won’t ask you to do as complicated
calculations as the HW
• Don’t need a calculator
• No books / notes
Maximum Likelihood Estimation
• How to apply the maximum likelihood
principle
– log likelihood + derivative + solve for 0
– You should know how to do this for Bernoulli trials
and 1-D Gaussian
• Conjugate distributions
– Dirichlet, Beta
Mixture Models and EM
• What does the EM algorithm do?
– Understand the E-step and M-step
• Log-exp-sum trick
– You should be able to derive this
– You should understand why we need to use it
Hidden Markov Models
• Viterbi
– What does it do?
– What is the running time?
• Forward-backward
– What does it do?
• Be able to compute the probability of a
“parse”
– Joint probability of a sequence of observed and
hidden states
Bayesian Networks
• Understand d-separation criteria
• Be able to answer simple questions about
whether variables are independent given
some evidence
• Markov Blanket
Markov Networks / Belief Propagation
• Moralizing a graph (convert Bayesian network
into Markov Network)
• Belief propagation
– What does it do, when is it guaranteed to
converge to the correct posterior distribution.