| Resources |
 Course General Information |
General information about the course and its assignments. |
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Book chapter about probability (in Hebrew) - Gal Chechnik et al |
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Amir's old notes on Bayesian Networks |
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Amir's old notes on Markov Networks |
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 Final exams from previous years |
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| 0. Probability and Graph Theory Background |
Recitation 0 - Probability and Graph Theory Background |
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Problem Set 0 - Probability |
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| 1. Graphical Models Representation |
Gal's Notes on Bayesian Networks |
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Recitation 1 - Bayesian Networks |
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Problem Set 1 - Bayesian Networks |
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 Problem Set 1 - Programming Assignment |
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Problem Set 1 - Partial Solution |
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Gal's short note on BN equivalence classes |
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Gal's notes on Markov Networks |
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Recitation 2 - Markov Networks |
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Gal's notes on Markov Networks II |
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Recitation 3 - BN and MN |
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Problem Set 2 - MNs and BNs (v2) |
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Problem Set 2 - Partial Solution |
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| 2. Exact and Approximate Inference |
Gal's Notes on Exact Inference |
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Recitation 4 - MAP and Loopy BP |
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 Factor product in python |
A python example explaining factor product. Disclaimer: there could be other implementations...
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Gal's notes on sampling |
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Gal's notes on Gibb's (didn't cover last page) |
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Recitation 5 - Sampling-based Inference |
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Recitation 6 - Amir's Notes on Mean Field |
Note that in Eq. 11-16 q_j(x_k) should be corrected to q_j(x_j). Also, please make sure you are familiar with Lagrange Multipliers. |
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Gal's notes on Metropolis Hastings |
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Problem Set 3 - Partial Solution |
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| 3. Parameter and Structure Learning |
Gal's notes on parameter learning |
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Gal's notes on structure learning |
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Recitation 7 - Structure Learning and TAN |
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Gal's notes on EM |
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Recitation 8 - Hidden Markov Model (old notes) |
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Recitation 8 - Hidden Markov Model - (Eitan's scanned notes) |
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Recitation 11 - Eitan's scanned notes |
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| 4. Reinforcement Learning |
Gal's notes on reinforcement learning (RL) |
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Intro to RL book by Sutton and Barto |
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Gal's forgotten page on Policy Improvement |
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Gal's notes on RL part 2 |
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Problem Set 6 - Partial Solution |
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