Index of /pub/educational/Coursera - Probabilistic Graphical Models/Lectures/
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Week 1 - 01 Introduction and Overview/ 23-Jul-2026 12:29 -
Week 1 - 02 Bayesian Network Fundamentals/ 23-Jul-2026 12:28 -
Week 1 - 03 Template Models/ 23-Jul-2026 12:28 -
Week 1 - 04 ML-class Octave Tutorial/ 23-Jul-2026 12:29 -
Week 2 - 05 Structured CPDs/ 23-Jul-2026 12:29 -
Week 2 - 06 Markov Network Fundamentals/ 23-Jul-2026 12:29 -
Week 3 - 07 Representation Wrapup-Knowledge Eng..> 23-Jul-2026 12:27 -
Week 3 - 08 Inference-Variable Elimination/ 23-Jul-2026 12:29 -
Week 3 - 09 Inference-Belief Propagation Part 1/ 23-Jul-2026 12:27 -
Week 4 - 10 Inference-Belief Propagation Part 2/ 23-Jul-2026 12:28 -
Week 4 - 11 Inference-MAP Estimation Part 1/ 23-Jul-2026 12:28 -
Week 5 - 12 Inference- MAP Estimation Part 2/ 23-Jul-2026 12:28 -
Week 5 - 13 Inference- Sampling Methods/ 23-Jul-2026 12:29 -
Week 6 - 14 Inference- Temporal Models and Wrap-up 23-Jul-2026 12:28 -
Week 6 - 15 Decision Theory/ 23-Jul-2026 12:29 -
Week 6 - 16 ML-class Revision/ 23-Jul-2026 12:28 -
Week 6 - 17 Learning-Overview/ 23-Jul-2026 12:25 -
Week 7 - 18 Learning- Parameter Estimation in BNs/ 23-Jul-2026 12:28 -
Week 7 - 19 Learning- Parameter Estimation in MNs/ 23-Jul-2026 12:25 -
Week 8 - 20 Structure Learning/ 23-Jul-2026 12:29 -
Week 9 - 21 Learning With Incomplete Data/ 23-Jul-2026 12:29 -
Week 9 - 22 Learning- Wrapup/ 23-Jul-2026 12:25 -
Week 9 - 23 Summary/ 23-Jul-2026 12:25 -