Midterm Review. The Midterm Everything we have talked about so far Stuff from HW I won’t ask you...
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Transcript of Midterm Review. The Midterm Everything we have talked about so far Stuff from HW I won’t ask you...
![Page 1: 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.](https://reader038.fdocuments.us/reader038/viewer/2022110207/56649d355503460f94a0babf/html5/thumbnails/1.jpg)
Midterm Review
![Page 2: 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.](https://reader038.fdocuments.us/reader038/viewer/2022110207/56649d355503460f94a0babf/html5/thumbnails/2.jpg)
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
![Page 3: 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.](https://reader038.fdocuments.us/reader038/viewer/2022110207/56649d355503460f94a0babf/html5/thumbnails/3.jpg)
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
![Page 4: 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.](https://reader038.fdocuments.us/reader038/viewer/2022110207/56649d355503460f94a0babf/html5/thumbnails/4.jpg)
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
![Page 5: 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.](https://reader038.fdocuments.us/reader038/viewer/2022110207/56649d355503460f94a0babf/html5/thumbnails/5.jpg)
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
![Page 6: 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.](https://reader038.fdocuments.us/reader038/viewer/2022110207/56649d355503460f94a0babf/html5/thumbnails/6.jpg)
Bayesian Networks
• Understand d-separation criteria• Be able to answer simple questions about
whether variables are independent given some evidence
• Markov Blanket
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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.