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Coursera - Probabilistic Graphical Models

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  1. Magnet Link：Magnet LinkMagnet Link
  2. File Size：1.43 GB
  3. Creat Time：2016-06-21
  4. Active Degree：457
  5. Last Active：2024-11-21
  6. File Tags：Coursera  Probabilistic  Graphical  Models  
  7. Statement：This site does not provide download links, only text displays, and does not contain any infringement.
File List:

    Coursera - Probabilistic Graphical Models

  1. Lectures/Week 7 - 19 Learning- Parameter Estimation in MNs/01_Maximum_Likelihood_for_Log-Linear_Models_28-47.mp4 35.43 MB
  2. Lectures/Week 9 - 23 Summary/01_Class_Summary_24-38.mp4 32.99 MB
  3. Lectures/Week 6 - 15 Decision Theory/01_Maximum_Expected_Utility_25-57.mp4 29.69 MB
  4. Lectures/Week 8 - 20 Structure Learning/06_Learning_General_Graphs-_Heuristic_Search_23-36.mp4 27.41 MB
  5. Lectures/Week 9 - 21 Learning With Incomplete Data/05_Latent_Variables_22-00.mp4 27.34 MB
  6. Lectures/Week 1 - 03 Template Models/02_Temporal_Models_-_DBNs_23-02.mp4 26.69 MB
  7. Lectures/Week 2 - 06 Markov Network Fundamentals/06_Log-Linear_Models_22-08.mp4 26.38 MB
  8. Lectures/Week 9 - 22 Learning- Wrapup/01_Summary-_Learning_20-11.mp4 26.30 MB
  9. Lectures/Week 2 - 06 Markov Network Fundamentals/03_Conditional_Random_Fields_22-22.mp4 25.66 MB
  10. Lectures/Week 9 - 21 Learning With Incomplete Data/01_Learning_With_Incomplete_Data_-_Overview_21-34.mp4 25.46 MB
  11. Lectures/Week 3 - 07 Representation Wrapup-Knowledge Engineering/01_Knowledge_Engineering_23-05.mp4 25.23 MB
  12. Lectures/Week 6 - 14 Inference- Temporal Models and Wrap-up/01_Inference_in_Temporal_Models_19-43.mp4 23.87 MB
  13. Lectures/Week 1 - 01 Introduction and Overview/02_Overview_and_Motivation_19-17.mp4 23.54 MB
  14. Lectures/Week 8 - 20 Structure Learning/04_Bayesian_Scores_20-35.mp4 23.16 MB
  15. Lectures/Week 1 - 03 Template Models/04_Plate_Models_20-08.mp4 23.02 MB
  16. Lectures/Week 2 - 06 Markov Network Fundamentals/05_I-maps_and_perfect_maps_20-59.mp4 22.93 MB
  17. Lectures/Week 1 - 02 Bayesian Network Fundamentals/05_Independencies_in_Bayesian_Networks_18-18.mp4 22.05 MB
  18. Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/05_Bayesian_Estimation_for_Bayesian_Networks_17-02.mp4 21.66 MB
  19. Lectures/Week 1 - 04 ML-class Octave Tutorial/02_Moving_Data_Around_16-07.mp4 21.27 MB
  20. Lectures/Week 6 - 15 Decision Theory/02_Utility_Functions_18-15.mp4 20.15 MB
  21. Lectures/Week 1 - 02 Bayesian Network Fundamentals/01_Semantics__Factorization_17-20.mp4 20.02 MB
  22. Lectures/Week 6 - 15 Decision Theory/03_Value_of_Perfect_Information_17-14.mp4 19.74 MB
  23. Lectures/Week 2 - 06 Markov Network Fundamentals/02_General_Gibbs_Distribution_15-52.mp4 19.38 MB
  24. Lectures/Week 8 - 20 Structure Learning/02_Likelihood_Scores_16-49.mp4 19.18 MB
  25. Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/03_Bayesian_Estimation_15-27.mp4 19.11 MB
  26. Lectures/Week 9 - 21 Learning With Incomplete Data/02_Expectation_Maximization_-_Intro_16-17.mp4 18.50 MB
  27. Lectures/Week 1 - 04 ML-class Octave Tutorial/01_Basic_Operations_13-59.mp4 18.14 MB
  28. Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/02_Maximum_Likelihood_Estimation_for_Bayesian_Networks_15-49.mp4 18.14 MB
  29. Lectures/Week 8 - 20 Structure Learning/07_Learning_General_Graphs-_Search_and_Decomposability_15-46.mp4 18.06 MB
  30. Lectures/Week 6 - 17 Learning-Overview/01_Learning-_Overview_15-35.mp4 17.93 MB
  31. Lectures/Week 5 - 13 Inference- Sampling Methods/05_Metropolis_Hastings_Algorithm_27-06.mp4 17.32 MB
  32. Lectures/Week 1 - 04 ML-class Octave Tutorial/05_Control_Statements-_for_while_if_statements_12-55.mp4 16.88 MB
  33. Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/04_Bayesian_Prediction_13-40.mp4 16.60 MB
  34. Lectures/Week 1 - 04 ML-class Octave Tutorial/06_Vectorization_13-48.mp4 16.48 MB
  35. Lectures/Week 2 - 05 Structured CPDs/02_Tree-Structured_CPDs_14-37.mp4 16.42 MB
  36. Lectures/Week 2 - 05 Structured CPDs/03_Independence_of_Causal_Influence_13-08.mp4 16.23 MB
  37. Lectures/Week 1 - 02 Bayesian Network Fundamentals/04_Conditional_Independence_12-38.mp4 15.88 MB
  38. Lectures/Week 1 - 02 Bayesian Network Fundamentals/03_Flow_of_Probabilistic_Influence_14-36.mp4 15.83 MB
  39. Lectures/Week 2 - 05 Structured CPDs/04_Continuous_Variables_13-25.mp4 15.70 MB
  40. Lectures/Week 1 - 04 ML-class Octave Tutorial/03_Computing_On_Data_13-15.mp4 15.62 MB
  41. Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/01_Maximum_Likelihood_Estimation_14-59.mp4 15.51 MB
  42. Lectures/Week 7 - 19 Learning- Parameter Estimation in MNs/02_Maximum_Likelihood_for_Conditional_Random_Fields_13-24.mp4 15.46 MB
  43. Lectures/Week 3 - 08 Inference-Variable Elimination/04_Complexity_of_Variable_Elimination_12-48.mp4 15.05 MB
  44. Lectures/Week 8 - 20 Structure Learning/05_Learning_Tree_Structured_Networks_12-05.mp4 14.80 MB
  45. Lectures/Week 6 - 14 Inference- Temporal Models and Wrap-up/02_Inference-_Summary_12-45.mp4 14.50 MB
  46. Lectures/Week 6 - 16 ML-class Revision/04_Model_Selection_and_Train_Validation_Test_Sets_12-03.mp4 14.41 MB
  47. Lectures/Week 5 - 13 Inference- Sampling Methods/01_Simple_Sampling_23-37.mp4 14.11 MB
  48. Lectures/Week 1 - 03 Template Models/03_Temporal_Models_-_HMMs_12-01.mp4 13.91 MB
  49. Lectures/Week 1 - 04 ML-class Octave Tutorial/04_Plotting_Data_09-38.mp4 13.64 MB
  50. Lectures/Week 3 - 09 Inference-Belief Propagation Part 1/01_Belief_Propagation_21-21.mp4 13.57 MB
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