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# A general outline of the class

This is really more of a list of topics than a schedule. It is subject to frequent change… please check back often as dates are likely to change.

Homework and Labs are due ON THE DATE where the homework or lab is linked. <!– PLACE HOLDER: Pruning Lab,,, Foundations Homework,MOM and MLE,Poisson,Estimators HW there are many useful Tutorials on the web, including this one at wikipedia–> <table border=1> <tr> <th>Date</th><th>Topic</th><th>Reading</th><th>Homework, Labs and Exams</th>

<tr> <th>4-26</th> <td>Subjective Probability</td> <!– Syllabus, Frequentist interpretation, Random Variable, Joint, Marginal, Conditional–> <td>Syllabus, Chapter 1</td> <td></td>

<tr> <th>4-27</th> <td>RV's, Independence, Bayes law</td> <!– Joint, Marginal, Conditional–> <td>Chapter 2</td> <td></td>

<tr> <th>4-29</th> <td></td> <td></td> <td>Probability Homework</td>

<tr><tr>

<tr> <th>5-2</th> <td>More distributions, Continuous RV's, Bayes with Continuous RV's (normal-normal)</td> <td>Sections 3.1-3.7</td> <td></td>

<tr> <th>5-3</th> <td>Bayes (Normal-Normal, Normal-Gamma)</td> <td>Sections 3.8, 4.1-4.7, 5.1-5.9 (Familiarize your self with these distributions), 8.6</td> <td></td>

<tr> <th>5-4</th> <td>Functions of random variables (Normal-IGamma), Conjugacy</td> <td></td> <td>RVs and Distributions HW</td>

<tr> <th>5-6</th> <td></td> <td></td> <td></td>

<tr><tr>

<tr> <th>5-9</th> <td>Beta-Binomial, Expectations</td> <td>Sections 6.1-6.4, 7.1-7.3</td> <td></td>

<tr> <th>5-10</th> <td>Estimators, Graphical models, Discrete and continuous, Inference in the Discrete Case</td> <td>Sections 7.5-7.7</td> <td>Conjugate Pair, and Functions of Random Variables</td>

<tr> <th>5-11</th> <td>Catch up</td> <td></td> <td></td>

<tr> <th>5-13</th> <td></td> <td></td> <td>Sampling, Estimators and GM's</td>

<tr><tr>

<tr> <th>5-16</th> <td>Temporal Models: Stochastic Processes, Markov Chains, Hidden Markov Models, Kalman filter</td> <td>Section 3.10</td> <td></td>

<tr> <th>5-17</th> <td>Sampling, Approximate Inference, Rejection, Likelihood Weighting, Particle Filter, Sampling Sample Code</td> <td>Sections 12.1-12.4</td> <td></td>

<tr> <th>5-18</th> <td>MCMC: Gibbs Sampling, Metropolis, Sampling Sample Code</td> <td>Section 12.5</td> <td></td>

<tr> <th>5-20</th> <td></td> <td></td> <td>Filters Lab</td>

<tr><tr>

<tr> <th>5-23</th> <td>Metropolis Proof, Parameter Learning</td> <td></td> <td>Gibbs Homework</td>

<tr> <th>5-24</th> <td>Structure Learning</td> <td></td> <td></td>

<tr> <th>5-25</th> <td>Utility and Decisions</td> <td>Section 6.1, Chapter 15, and </td> <td></td>

<tr> <th>5-27</th> <td></td> <td></td> <td></td>

<tr><tr>

<tr> <th>5-30</th> <td>Holiday, no class</td> <td></td> <td></td>

<tr> <th>5-31</th> <td>Value of Information</td> <td>EVSI</td> <td></td>

<tr> <th>6-1</th> <td>infinite models if time permits</td> <td></td> <td>Individual MCMC Lab– This is not a group project. See the Syllabus for more info.</td>

<tr> <th>6-3</th> <td></td> <td></td> <td></td>

<tr><tr>

<tr> <th>6-6</th> <td>Graphical models part two, d-separability, pruning</td> <td>Pruning and d-separability</td> <td></td>

<tr> <th>6-7</th> <td>No class, use this time for labs</td> <td></td> <td>Learning Lab</td>

<tr> <th>6-8</th> <td>Continuous EVSI</td> <td></td> <td></td>

<tr> <th>6-10</th> <td></td> <td></td> <td></td>

<tr><tr>

<tr> <th>6-13</th> <td>Open Discussion of the labs and Final</td> <td></td> <td>Pruning Lab Note also that I can not accept anything except the Final after this.</td>

<tr> <th>6-16</th> <td>The Final will be a take home exam. It will be posted here: Final. Note that it (1) must be turned in on time, I cannot accept it late and (2) must be done independently not as a group or pair, no discussion of any kind.</td> <td></td> <td></td>

</table>