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 — cs677:schedule [2014/12/05 00:16] (current)ryancha created 2014/12/05 00:16 ryancha created 2014/12/05 00:16 ryancha created Line 1: Line 1: + ​ + = 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. + ​ + ​ +
+ <​th>​Date<​th>​Topic<​th>​Reading<​th>​Homework,​ Labs and Exams​ + + + <​th>​4-26​ + <​td>​[[Subjective Probability]]​ + ​ + <​td>​[[Syllabus]],​ Chapter 1​ + <​td>​ + + + <​th>​4-27​ + <​td>​RV'​s,​ Independence,​ Bayes law​ + ​ + <​td>​Chapter 2​ + <​td>​ + + + <​th>​4-29​ + <​td>​ + <​td>​ + <​td>​[[Probability Homework]]​ + + <​tr><​tr>​ + + + <​th>​5-2​ + <​td>​More distributions,​ Continuous RV's, Bayes with Continuous RV's (normal-normal)​ + <​td>​Sections 3.1-3.7​ + <​td>​ + + + <​th>​5-3​ + <​td>​Bayes (Normal-Normal,​ Normal-Gamma)​ + <​td>​Sections 3.8, 4.1-4.7, 5.1-5.9 (Familiarize your self with these distributions),​ 8.6​ + <​td>​ + + + <​th>​5-4​ + <​td>​Functions of random variables (Normal-IGamma),​ Conjugacy​ + <​td>​ + <​td>​[[RVs and Distributions HW]]​ + + + <​th>​5-6​ + <​td>​ + <​td>​ + <​td>​ + + <​tr><​tr>​ + + + <​th>​5-9​ + <​td>​Beta-Binomial,​ Expectations​ + <​td>​Sections 6.1-6.4, 7.1-7.3​ + <​td>​ + + + <​th>​5-10​ + <​td>​Estimators,​ Graphical models, Discrete and continuous, Inference in the Discrete Case​ + <​td>​Sections 7.5-7.7​ + <​td>​[[Conjugate Pair, and Functions of Random Variables]]​ + + + <​th>​5-11​ + <​td>​Catch up​ + <​td>​ + <​td>​ + + + <​th>​5-13​ + <​td>​ + <​td>​ + <​td>​[[Sampling,​ Estimators and GM'​s]]​ + + <​tr><​tr>​ + + + <​th>​5-16​ + <​td>​Temporal Models: Stochastic Processes, Markov Chains, Hidden Markov Models, Kalman filter​ + <​td>​Section 3.10​ + <​td>​ + + + <​th>​5-17​ + <​td>​Sampling,​ Approximate Inference, Rejection, Likelihood Weighting, Particle Filter, [[Sampling Sample Code]]​ + <​td>​Sections 12.1-12.4​ + <​td>​ + + + <​th>​5-18​ + <​td>​MCMC:​ Gibbs Sampling, Metropolis, [[Sampling Sample Code]]​ + <​td>​Section 12.5​ + <​td>​ + + + <​th>​5-20​ + <​td>​ + <​td>​ + <​td>​[[Filters Lab]]​ + + <​tr><​tr>​ + + + <​th>​5-23​ + <​td>​Metropolis Proof, Parameter Learning​ + <​td>​ + <​td>​[[Gibbs Homework]]​ + + + <​th>​5-24​ + <​td>​Structure Learning​ + <​td>​ + <​td>​ + + + <​th>​5-25​ + <​td>​Utility and Decisions​ + <​td>​Section 6.1, Chapter 15, and + <​td>​ + + + <​th>​5-27​ + <​td>​ + <​td>​ + <​td>​ + + <​tr><​tr>​ + + + <​th>​5-30​ + <​td>​Holiday,​ no class​ + <​td>​ + <​td>​ + + + <​th>​5-31​ + <​td>​Value of Information​ + <​td>​[http://​aml.cs.byu.edu/​~kseppi/​cs470sp10files/​evsi_v4.pdf EVSI]​ + <​td>​ + + + <​th>​6-1​ + <​td>​infinite models if time permits​ + <​td>​ + <​td>'''​Individual'''​ [[MCMC Lab]]-- This is not a group project. See the [[Syllabus]] for more info.​ + + + <​th>​6-3​ + <​td>​ + <​td>​ + <​td>​ + + <​tr><​tr>​ + + + <​th>​6-6​ + <​td>​Graphical models part two, d-separability,​ pruning​ + <​td>​[[Pruning and d-separability]]​ + <​td>​ + + + <​th>​6-7​ + <​td>​No class, use this time for labs​ + <​td>​ + <​td>​[[Learning Lab]]​ + + + <​th>​6-8​ + <​td>​[[Continuous EVSI]]​ + <​td>​ + <​td>​ + + + <​th>​6-10​ + <​td>​ + <​td>​ + <​td>​ + + <​tr><​tr>​ + + + <​th>​6-13​ + <​td>​Open Discussion of the labs and Final​ + <​td>​ + <​td>​[[Pruning Lab]] Note also that I can not accept anything except the Final after this.​ + + + <​th>​6-16​ + <​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>​ + + ​ + + [[Helpful Links]]