Coursera - Probabilistic Graphical Models

Creation Time	Sept. 21, 2023, 2:36 a.m.
Last Access Time	Nov. 12, 2024, 10:43 p.m.
File Size	1.4 GB
Keywords	Models - Graphical Probabilistic Coursera
Total Requests	154
Total Files	547
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Lectures/Week 7 - 19 Learning- Parameter Estimation in MNs/01_Maximum_Likelihood_for_Log-Linear_Models_28-47.mp4 34.6 MB
Lectures/Week 9 - 23 Summary/01_Class_Summary_24-38.mp4 32.2 MB
Lectures/Week 6 - 15 Decision Theory/01_Maximum_Expected_Utility_25-57.mp4 29.0 MB
Lectures/Week 8 - 20 Structure Learning/06_Learning_General_Graphs-_Heuristic_Search_23-36.mp4 26.8 MB
Lectures/Week 9 - 21 Learning With Incomplete Data/05_Latent_Variables_22-00.mp4 26.7 MB
Lectures/Week 1 - 03 Template Models/02_Temporal_Models_-_DBNs_23-02.mp4 26.1 MB
Lectures/Week 2 - 06 Markov Network Fundamentals/06_Log-Linear_Models_22-08.mp4 25.8 MB
Lectures/Week 9 - 22 Learning- Wrapup/01_Summary-_Learning_20-11.mp4 25.7 MB
Lectures/Week 2 - 06 Markov Network Fundamentals/03_Conditional_Random_Fields_22-22.mp4 25.1 MB
Lectures/Week 9 - 21 Learning With Incomplete Data/01_Learning_With_Incomplete_Data_-_Overview_21-34.mp4 24.9 MB
Lectures/Week 3 - 07 Representation Wrapup-Knowledge Engineering/01_Knowledge_Engineering_23-05.mp4 24.6 MB
Lectures/Week 6 - 14 Inference- Temporal Models and Wrap-up/01_Inference_in_Temporal_Models_19-43.mp4 23.3 MB
Lectures/Week 1 - 01 Introduction and Overview/02_Overview_and_Motivation_19-17.mp4 23.0 MB
Lectures/Week 8 - 20 Structure Learning/04_Bayesian_Scores_20-35.mp4 22.6 MB
Lectures/Week 1 - 03 Template Models/04_Plate_Models_20-08.mp4 22.5 MB
Lectures/Week 2 - 06 Markov Network Fundamentals/05_I-maps_and_perfect_maps_20-59.mp4 22.4 MB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/05_Independencies_in_Bayesian_Networks_18-18.mp4 21.5 MB
Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/05_Bayesian_Estimation_for_Bayesian_Networks_17-02.mp4 21.2 MB
Lectures/Week 1 - 04 ML-class Octave Tutorial/02_Moving_Data_Around_16-07.mp4 20.8 MB
Lectures/Week 6 - 15 Decision Theory/02_Utility_Functions_18-15.mp4 19.7 MB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/01_Semantics__Factorization_17-20.mp4 19.6 MB
Lectures/Week 6 - 15 Decision Theory/03_Value_of_Perfect_Information_17-14.mp4 19.3 MB
Lectures/Week 2 - 06 Markov Network Fundamentals/02_General_Gibbs_Distribution_15-52.mp4 18.9 MB
Lectures/Week 8 - 20 Structure Learning/02_Likelihood_Scores_16-49.mp4 18.7 MB
Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/03_Bayesian_Estimation_15-27.mp4 18.7 MB
Lectures/Week 9 - 21 Learning With Incomplete Data/02_Expectation_Maximization_-_Intro_16-17.mp4 18.1 MB
Lectures/Week 1 - 04 ML-class Octave Tutorial/01_Basic_Operations_13-59.mp4 17.7 MB
Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/02_Maximum_Likelihood_Estimation_for_Bayesian_Networks_15-49.mp4 17.7 MB
Lectures/Week 8 - 20 Structure Learning/07_Learning_General_Graphs-_Search_and_Decomposability_15-46.mp4 17.6 MB
Lectures/Week 6 - 17 Learning-Overview/01_Learning-_Overview_15-35.mp4 17.5 MB
Lectures/Week 5 - 13 Inference- Sampling Methods/05_Metropolis_Hastings_Algorithm_27-06.mp4 16.9 MB
Lectures/Week 1 - 04 ML-class Octave Tutorial/05_Control_Statements-_for_while_if_statements_12-55.mp4 16.5 MB
Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/04_Bayesian_Prediction_13-40.mp4 16.2 MB
Lectures/Week 1 - 04 ML-class Octave Tutorial/06_Vectorization_13-48.mp4 16.1 MB
Lectures/Week 2 - 05 Structured CPDs/02_Tree-Structured_CPDs_14-37.mp4 16.0 MB
Lectures/Week 2 - 05 Structured CPDs/03_Independence_of_Causal_Influence_13-08.mp4 15.9 MB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/04_Conditional_Independence_12-38.mp4 15.5 MB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/03_Flow_of_Probabilistic_Influence_14-36.mp4 15.5 MB
Lectures/Week 2 - 05 Structured CPDs/04_Continuous_Variables_13-25.mp4 15.3 MB
Lectures/Week 1 - 04 ML-class Octave Tutorial/03_Computing_On_Data_13-15.mp4 15.3 MB
Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/01_Maximum_Likelihood_Estimation_14-59.mp4 15.1 MB
Lectures/Week 7 - 19 Learning- Parameter Estimation in MNs/02_Maximum_Likelihood_for_Conditional_Random_Fields_13-24.mp4 15.1 MB
Lectures/Week 3 - 08 Inference-Variable Elimination/04_Complexity_of_Variable_Elimination_12-48.mp4 14.7 MB
Lectures/Week 8 - 20 Structure Learning/05_Learning_Tree_Structured_Networks_12-05.mp4 14.5 MB
Lectures/Week 6 - 14 Inference- Temporal Models and Wrap-up/02_Inference-_Summary_12-45.mp4 14.2 MB
Lectures/Week 6 - 16 ML-class Revision/04_Model_Selection_and_Train_Validation_Test_Sets_12-03.mp4 14.1 MB
Lectures/Week 5 - 13 Inference- Sampling Methods/01_Simple_Sampling_23-37.mp4 13.8 MB
Lectures/Week 1 - 03 Template Models/03_Temporal_Models_-_HMMs_12-01.mp4 13.6 MB
Lectures/Week 1 - 04 ML-class Octave Tutorial/04_Plotting_Data_09-38.mp4 13.3 MB
Lectures/Week 3 - 09 Inference-Belief Propagation Part 1/01_Belief_Propagation_21-21.mp4 13.3 MB
Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/07_Loopy_BP_and_Message_Decoding_21-42.mp4 13.2 MB
Lectures/Week 9 - 21 Learning With Incomplete Data/03_Analysis_of_EM_Algorithm_11-32.mp4 12.9 MB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/08_Knowledge_Engineering_Example_-_SAMIAM_14-14.mp4 12.8 MB
Lectures/Week 9 - 21 Learning With Incomplete Data/04_EM_in_Practice_11-17.mp4 12.7 MB
Lectures/Week 4 - 11 Inference-MAP Estimation Part 1/01_Max_Sum_Message_Passing_20-27.mp4 12.6 MB
Lectures/Week 6 - 16 ML-class Revision/06_Regularization_and_Bias_Variance_11-20.mp4 12.6 MB
Lectures/Week 2 - 06 Markov Network Fundamentals/01_Pairwise_Markov_Networks_10-59.mp4 12.6 MB
Lectures/Week 8 - 20 Structure Learning/03_BIC_and_Asymptotic_Consistency_11-26.mp4 12.5 MB
Lectures/Week 5 - 13 Inference- Sampling Methods/04_Gibbs_Sampling_19-26.mp4 12.5 MB
Lectures/Week 6 - 16 ML-class Revision/02_Regularization-_Cost_Function_10-10.mp4 11.6 MB
Lectures/Week 1 - 03 Template Models/01_Overview_of_Template_Models_10-55.mp4 11.6 MB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/07_Application_-_Medical_Diagnosis_09-19.mp4 11.5 MB
Lectures/Week 7 - 19 Learning- Parameter Estimation in MNs/03_MAP_Estimation_for_MRFs_and_CRFs_9-59.mp4 11.3 MB
Lectures/Week 5 - 12 Inference- MAP Estimation Part 2/02_Dual_Decomposition_-_Intuition_17-46.mp4 11.2 MB
Lectures/Week 6 - 16 ML-class Revision/01_Regularization-_The_Problem_of_Overfitting_09-42.mp4 11.1 MB
Lectures/Week 3 - 08 Inference-Variable Elimination/03_Variable_Elimination_Algorithm_16-17.mp4 11.1 MB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/02_Reasoning_Patterns_09-59.mp4 10.8 MB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/06_Naive_Bayes_09-52.mp4 10.6 MB
Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/05_Clique_Trees_and_VE_16-17.mp4 10.6 MB
Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/02_Clique_Tree_Algorithm_-_Correctness_18-23.mp4 10.5 MB
Lectures/Week 2 - 06 Markov Network Fundamentals/07_Shared_Features_in_Log-Linear_Models_08-28.mp4 10.0 MB
Lectures/Week 5 - 12 Inference- MAP Estimation Part 2/03_Dual_Decomposition_-_Algorithm_16-16.mp4 9.7 MB
Lectures/Week 3 - 09 Inference-Belief Propagation Part 1/02_Properties_of_Cluster_Graphs_15-00.mp4 9.7 MB
Lectures/Week 5 - 12 Inference- MAP Estimation Part 2/01_Tractable_MAP_Problems_15-04.mp4 9.7 MB
Lectures/Week 2 - 05 Structured CPDs/01_Overview-_Structured_CPDs_08-00.mp4 9.6 MB
Lectures/Week 3 - 08 Inference-Variable Elimination/05_Graph-Based_Perspective_on_Variable_Elimination_15-25.mp4 9.5 MB
Lectures/Week 5 - 13 Inference- Sampling Methods/03_Using_a_Markov_Chain_15-27.mp4 9.5 MB
Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/04_Clique_Trees_and_Independence_15-21.mp4 9.5 MB
Lectures/Week 5 - 13 Inference- Sampling Methods/02_Markov_Chain_Monte_Carlo_14-18.mp4 9.2 MB
Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/06_BP_In_Practice_15-38.mp4 9.2 MB
Lectures/Week 3 - 08 Inference-Variable Elimination/01_Overview-_Conditional_Probability_Queries_15-22.mp4 9.0 MB
Lectures/Week 6 - 16 ML-class Revision/05_Diagnosing_Bias_vs_Variance_07-42.mp4 9.0 MB
Lectures/Week 3 - 08 Inference-Variable Elimination/06_Finding_Elimination_Orderings_11-58.mp4 8.8 MB
Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/03_Clique_Tree_Algorithm_-_Computation_16-18.mp4 8.7 MB
Lectures/Week 6 - 16 ML-class Revision/03_Evaluating_a_Hypothesis_07-35.mp4 8.5 MB
Lectures/Week 1 - 01 Introduction and Overview/04_Factors_06-40.mp4 7.4 MB
Lectures/Week 1 - 01 Introduction and Overview/01_Welcome_05-35.mp4 7.1 MB
Lectures/Week 8 - 20 Structure Learning/01_Structure_Learning_Overview_5-49.mp4 6.7 MB
Lectures/Week 3 - 08 Inference-Variable Elimination/02_Overview-_MAP_Inference_09-42.mp4 5.9 MB
Lectures/Week 2 - 06 Markov Network Fundamentals/04_Independencies_in_Markov_Networks_04-48.mp4 5.8 MB
Lectures/Week 1 - 01 Introduction and Overview/03_Distributions_04-56.mp4 5.8 MB
Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/01_Properties_of_Belief_Propagation_9-31.mp4 5.8 MB
Lectures/Week 1 - 04 ML-class Octave Tutorial/07_Working_on_and_Submitting_Programming_Exercises_03-33.mp4 5.5 MB
Assignments/Assignment 3/inference/doinference.exe 3.3 MB
Lectures/Week 4 - 11 Inference-MAP Estimation Part 1/02_Finding_a_MAP_Assignment_3-57.mp4 2.7 MB
Assignments/Assignment 9/PA9Data.mat 2.6 MB
Assignments/Assignment 3/inference/doinference-linux 2.2 MB
Assignments/Assignment 3/inference/inference-src.zip 2.1 MB
Assignments/Assignment 2/PA2Description.pdf 1.3 MB
Assignments/Assignment 9/PA9SampleCases.mat 1.0 MB
Assignments/Assignment 9/PA9Description.pdf 987.2 KB
Assignments/Assignment 9/submit_input.mat 841.5 KB
Assignments/Assignment 3/inference/doinference-mac 816.3 KB
Assignments/Assignment 5/Assignment 5.pdf 522.5 KB
Assignments/Assignment 7/PA7Description.pdf 487.1 KB
Assignments/Assignment 6/Assignment 6.pdf 456.3 KB
Assignments/Assignment 8/submit_input.mat 453.1 KB
Assignments/Assignment 4/Assignment 4.pdf 421.3 KB
Assignments/Assignment 3/PA3Description.pdf 413.3 KB
Assignments/Assignment 8/PA8Description.pdf 378.2 KB
Assignments/Assignment 8/PA8SampleCases.mat 287.7 KB
Assignments/Assignment 1/Assignment 1.pdf 276.0 KB
Assignments/Assignment 4/PA4Sample.mat 215.3 KB
Assignments/Assignment 8/PA8Data.mat 191.4 KB
Assignments/Assignment 2/PA2Appendix.pdf 98.1 KB
Assignments/Assignment 7/Part2Sample.mat 84.7 KB
Assignments/Assignment 3/PA3SampleCases.mat 74.3 KB
Assignments/Assignment 4/PA4Test.mat 63.0 KB
Assignments/Assignment 3/PA3Models.mat 53.6 KB
Assignments/Assignment 5/exampleIOPA5.mat 43.6 KB
Assignments/Assignment 5/submit.m 36.9 KB
Lectures/Week 5 - 13 Inference- Sampling Methods/05_Metropolis_Hastings_Algorithm_27-06.srt 32.5 KB
Lectures/Week 7 - 19 Learning- Parameter Estimation in MNs/01_Maximum_Likelihood_for_Log-Linear_Models_28-47.srt 30.9 KB
Lectures/Week 8 - 20 Structure Learning/06_Learning_General_Graphs-_Heuristic_Search_23-36.srt 30.2 KB
Lectures/Week 6 - 15 Decision Theory/01_Maximum_Expected_Utility_25-57.srt 29.8 KB
Assignments/Assignment 6/submit.m 28.3 KB
Assignments/Assignment 4/submit.m 28.2 KB
Lectures/Week 3 - 07 Representation Wrapup-Knowledge Engineering/01_Knowledge_Engineering_23-05.srt 28.2 KB
Lectures/Week 2 - 06 Markov Network Fundamentals/06_Log-Linear_Models_22-08.srt 26.8 KB
Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/07_Loopy_BP_and_Message_Decoding_21-42.srt 26.5 KB
Lectures/Week 1 - 03 Template Models/02_Temporal_Models_-_DBNs_23-02.srt 26.3 KB
Assignments/Assignment 2/submit.m 26.2 KB
Lectures/Week 5 - 13 Inference- Sampling Methods/01_Simple_Sampling_23-37.srt 26.2 KB
Lectures/Week 9 - 21 Learning With Incomplete Data/05_Latent_Variables_22-00.srt 25.3 KB
Lectures/Week 6 - 14 Inference- Temporal Models and Wrap-up/01_Inference_in_Temporal_Models_19-43.srt 24.8 KB
Lectures/Week 1 - 01 Introduction and Overview/02_Overview_and_Motivation_19-17.srt 24.7 KB
Lectures/Week 9 - 21 Learning With Incomplete Data/01_Learning_With_Incomplete_Data_-_Overview_21-34.srt 24.5 KB
Assignments/Assignment 8/submit.m 23.9 KB
Lectures/Week 3 - 09 Inference-Belief Propagation Part 1/01_Belief_Propagation_21-21.srt 23.8 KB
Lectures/Week 8 - 20 Structure Learning/04_Bayesian_Scores_20-35.srt 23.8 KB
Lectures/Week 1 - 03 Template Models/04_Plate_Models_20-08.srt 23.5 KB
Lectures/Week 2 - 06 Markov Network Fundamentals/03_Conditional_Random_Fields_22-22.srt 23.4 KB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/08_Knowledge_Engineering_Example_-_SAMIAM_14-14.srt 23.0 KB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/05_Independencies_in_Bayesian_Networks_18-18.srt 22.9 KB
Assignments/Assignment 1/submit.m 22.4 KB
Lectures/Week 2 - 06 Markov Network Fundamentals/05_I-maps_and_perfect_maps_20-59.srt 22.3 KB
Lectures/Week 4 - 11 Inference-MAP Estimation Part 1/01_Max_Sum_Message_Passing_20-27.srt 22.3 KB
Lectures/Week 5 - 13 Inference- Sampling Methods/05_Metropolis_Hastings_Algorithm_27-06.txt 22.2 KB
Assignments/Assignment 9/submit.m 22.1 KB
Lectures/Week 6 - 15 Decision Theory/03_Value_of_Perfect_Information_17-14.srt 21.6 KB
Lectures/Week 7 - 19 Learning- Parameter Estimation in MNs/01_Maximum_Likelihood_for_Log-Linear_Models_28-47.txt 21.1 KB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/01_Semantics__Factorization_17-20.srt 21.1 KB
Lectures/Week 6 - 15 Decision Theory/02_Utility_Functions_18-15.srt 21.0 KB
Lectures/Week 8 - 20 Structure Learning/06_Learning_General_Graphs-_Heuristic_Search_23-36.txt 20.7 KB
Assignments/Assignment 3/submit.m 20.6 KB
Assignments/Assignment 7/submit.m 20.5 KB
Lectures/Week 6 - 15 Decision Theory/01_Maximum_Expected_Utility_25-57.txt 20.4 KB
Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/02_Clique_Tree_Algorithm_-_Correctness_18-23.srt 20.1 KB
Lectures/Week 9 - 21 Learning With Incomplete Data/02_Expectation_Maximization_-_Intro_16-17.srt 20.0 KB
Lectures/Week 5 - 12 Inference- MAP Estimation Part 2/02_Dual_Decomposition_-_Intuition_17-46.srt 19.6 KB
Lectures/Week 5 - 13 Inference- Sampling Methods/04_Gibbs_Sampling_19-26.srt 19.6 KB
Lectures/Week 6 - 17 Learning-Overview/01_Learning-_Overview_15-35.srt 19.5 KB
Lectures/Week 5 - 12 Inference- MAP Estimation Part 2/01_Tractable_MAP_Problems_15-04.srt 18.9 KB
Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/05_Bayesian_Estimation_for_Bayesian_Networks_17-02.srt 18.9 KB
Lectures/Week 8 - 20 Structure Learning/02_Likelihood_Scores_16-49.srt 18.8 KB
Lectures/Week 1 - 04 ML-class Octave Tutorial/02_Moving_Data_Around_16-07.srt 18.6 KB
Lectures/Week 5 - 12 Inference- MAP Estimation Part 2/03_Dual_Decomposition_-_Algorithm_16-16.srt 18.4 KB
Lectures/Week 3 - 07 Representation Wrapup-Knowledge Engineering/01_Knowledge_Engineering_23-05.txt 18.3 KB
Assignments/Assignment 7/Part2Test.mat 18.2 KB
Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/07_Loopy_BP_and_Message_Decoding_21-42.txt 18.1 KB
Lectures/Week 1 - 03 Template Models/02_Temporal_Models_-_DBNs_23-02.txt 18.0 KB
Lectures/Week 5 - 13 Inference- Sampling Methods/01_Simple_Sampling_23-37.txt 17.9 KB
Lectures/Week 5 - 13 Inference- Sampling Methods/03_Using_a_Markov_Chain_15-27.srt 17.9 KB
Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/03_Bayesian_Estimation_15-27.srt 17.7 KB
Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/05_Clique_Trees_and_VE_16-17.srt 17.7 KB
Lectures/Week 3 - 08 Inference-Variable Elimination/03_Variable_Elimination_Algorithm_16-17.srt 17.5 KB
Lectures/Week 3 - 08 Inference-Variable Elimination/01_Overview-_Conditional_Probability_Queries_15-22.srt 17.4 KB
Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/06_BP_In_Practice_15-38.srt 17.3 KB
Lectures/Week 9 - 21 Learning With Incomplete Data/05_Latent_Variables_22-00.txt 17.3 KB
Lectures/Week 5 - 13 Inference- Sampling Methods/02_Markov_Chain_Monte_Carlo_14-18.srt 17.0 KB
Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/04_Clique_Trees_and_Independence_15-21.srt 16.9 KB
Lectures/Week 6 - 14 Inference- Temporal Models and Wrap-up/01_Inference_in_Temporal_Models_19-43.txt 16.9 KB
Lectures/Week 1 - 01 Introduction and Overview/02_Overview_and_Motivation_19-17.txt 16.8 KB
Lectures/Week 9 - 21 Learning With Incomplete Data/01_Learning_With_Incomplete_Data_-_Overview_21-34.txt 16.8 KB
Lectures/Week 2 - 05 Structured CPDs/02_Tree-Structured_CPDs_14-37.srt 16.8 KB
Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/02_Maximum_Likelihood_Estimation_for_Bayesian_Networks_15-49.srt 16.7 KB
Lectures/Week 1 - 04 ML-class Octave Tutorial/06_Vectorization_13-48.srt 16.7 KB
Lectures/Week 2 - 06 Markov Network Fundamentals/06_Log-Linear_Models_22-08.txt 16.6 KB
Lectures/Week 3 - 09 Inference-Belief Propagation Part 1/02_Properties_of_Cluster_Graphs_15-00.srt 16.5 KB
Lectures/Week 1 - 04 ML-class Octave Tutorial/01_Basic_Operations_13-59.srt 16.4 KB
Lectures/Week 6 - 14 Inference- Temporal Models and Wrap-up/02_Inference-_Summary_12-45.srt 16.4 KB
Lectures/Week 3 - 09 Inference-Belief Propagation Part 1/01_Belief_Propagation_21-21.txt 16.3 KB
Lectures/Week 8 - 20 Structure Learning/04_Bayesian_Scores_20-35.txt 16.3 KB
Lectures/Week 2 - 06 Markov Network Fundamentals/02_General_Gibbs_Distribution_15-52.srt 16.2 KB
Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/03_Clique_Tree_Algorithm_-_Computation_16-18.srt 16.1 KB
Lectures/Week 6 - 16 ML-class Revision/04_Model_Selection_and_Train_Validation_Test_Sets_12-03.srt 16.0 KB
Lectures/Week 1 - 03 Template Models/04_Plate_Models_20-08.txt 16.0 KB
Lectures/Week 2 - 06 Markov Network Fundamentals/03_Conditional_Random_Fields_22-22.txt 16.0 KB
Lectures/Week 1 - 04 ML-class Octave Tutorial/03_Computing_On_Data_13-15.srt 15.9 KB
Lectures/Week 7 - 19 Learning- Parameter Estimation in MNs/02_Maximum_Likelihood_for_Conditional_Random_Fields_13-24.srt 15.8 KB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/05_Independencies_in_Bayesian_Networks_18-18.txt 15.7 KB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/03_Flow_of_Probabilistic_Influence_14-36.srt 15.5 KB
Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/01_Maximum_Likelihood_Estimation_14-59.srt 15.4 KB
Lectures/Week 4 - 11 Inference-MAP Estimation Part 1/01_Max_Sum_Message_Passing_20-27.txt 15.2 KB
Lectures/Week 2 - 06 Markov Network Fundamentals/05_I-maps_and_perfect_maps_20-59.txt 15.2 KB
Lectures/Week 9 - 21 Learning With Incomplete Data/04_EM_in_Practice_11-17.srt 15.1 KB
Lectures/Week 1 - 03 Template Models/03_Temporal_Models_-_HMMs_12-01.srt 15.1 KB
Lectures/Week 1 - 04 ML-class Octave Tutorial/05_Control_Statements-_for_while_if_statements_12-55.srt 15.1 KB
Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/04_Bayesian_Prediction_13-40.srt 15.0 KB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/04_Conditional_Independence_12-38.srt 15.0 KB
Lectures/Week 6 - 15 Decision Theory/03_Value_of_Perfect_Information_17-14.txt 15.0 KB
Lectures/Week 6 - 16 ML-class Revision/06_Regularization_and_Bias_Variance_11-20.srt 14.8 KB
Lectures/Week 3 - 08 Inference-Variable Elimination/05_Graph-Based_Perspective_on_Variable_Elimination_15-25.srt 14.8 KB
Lectures/Week 2 - 05 Structured CPDs/04_Continuous_Variables_13-25.srt 14.5 KB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/01_Semantics__Factorization_17-20.txt 14.4 KB
Assignments/Assignment 3/PA3Data.mat 14.4 KB
Lectures/Week 6 - 15 Decision Theory/02_Utility_Functions_18-15.txt 14.4 KB
Lectures/Week 3 - 08 Inference-Variable Elimination/06_Finding_Elimination_Orderings_11-58.srt 14.1 KB
Lectures/Week 8 - 20 Structure Learning/05_Learning_Tree_Structured_Networks_12-05.srt 13.9 KB
Lectures/Week 2 - 05 Structured CPDs/03_Independence_of_Causal_Influence_13-08.srt 13.9 KB
Lectures/Week 9 - 21 Learning With Incomplete Data/02_Expectation_Maximization_-_Intro_16-17.txt 13.7 KB
Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/02_Clique_Tree_Algorithm_-_Correctness_18-23.txt 13.7 KB
Lectures/Week 8 - 20 Structure Learning/03_BIC_and_Asymptotic_Consistency_11-26.srt 13.6 KB
Lectures/Week 2 - 06 Markov Network Fundamentals/01_Pairwise_Markov_Networks_10-59.srt 13.5 KB
Lectures/Week 5 - 12 Inference- MAP Estimation Part 2/02_Dual_Decomposition_-_Intuition_17-46.txt 13.4 KB
Lectures/Week 5 - 13 Inference- Sampling Methods/04_Gibbs_Sampling_19-26.txt 13.4 KB
Lectures/Week 6 - 17 Learning-Overview/01_Learning-_Overview_15-35.txt 13.3 KB
Lectures/Week 9 - 21 Learning With Incomplete Data/03_Analysis_of_EM_Algorithm_11-32.srt 13.1 KB
Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/05_Bayesian_Estimation_for_Bayesian_Networks_17-02.txt 13.0 KB
Lectures/Week 5 - 12 Inference- MAP Estimation Part 2/01_Tractable_MAP_Problems_15-04.txt 12.9 KB
Lectures/Week 6 - 16 ML-class Revision/01_Regularization-_The_Problem_of_Overfitting_09-42.srt 12.9 KB
Lectures/Week 8 - 20 Structure Learning/02_Likelihood_Scores_16-49.txt 12.9 KB
Lectures/Week 3 - 08 Inference-Variable Elimination/04_Complexity_of_Variable_Elimination_12-48.srt 12.9 KB
Lectures/Week 6 - 16 ML-class Revision/02_Regularization-_Cost_Function_10-10.srt 12.8 KB
Lectures/Week 1 - 04 ML-class Octave Tutorial/02_Moving_Data_Around_16-07.txt 12.7 KB
Lectures/Week 1 - 03 Template Models/01_Overview_of_Template_Models_10-55.srt 12.7 KB
Lectures/Week 5 - 12 Inference- MAP Estimation Part 2/03_Dual_Decomposition_-_Algorithm_16-16.txt 12.6 KB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/08_Knowledge_Engineering_Example_-_SAMIAM_14-14.txt 12.5 KB
Lectures/Week 7 - 19 Learning- Parameter Estimation in MNs/03_MAP_Estimation_for_MRFs_and_CRFs_9-59.srt 12.4 KB
Lectures/Week 5 - 13 Inference- Sampling Methods/03_Using_a_Markov_Chain_15-27.txt 12.2 KB
Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/05_Clique_Trees_and_VE_16-17.txt 12.1 KB
Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/03_Bayesian_Estimation_15-27.txt 12.1 KB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/07_Application_-_Medical_Diagnosis_09-19.srt 12.1 KB
Lectures/Week 3 - 08 Inference-Variable Elimination/03_Variable_Elimination_Algorithm_16-17.txt 12.0 KB
Lectures/Week 3 - 08 Inference-Variable Elimination/01_Overview-_Conditional_Probability_Queries_15-22.txt 11.9 KB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/02_Reasoning_Patterns_09-59.srt 11.9 KB
Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/06_BP_In_Practice_15-38.txt 11.8 KB
Lectures/Week 5 - 13 Inference- Sampling Methods/02_Markov_Chain_Monte_Carlo_14-18.txt 11.6 KB
Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/04_Clique_Trees_and_Independence_15-21.txt 11.6 KB
Lectures/Week 2 - 05 Structured CPDs/02_Tree-Structured_CPDs_14-37.txt 11.5 KB
Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/02_Maximum_Likelihood_Estimation_for_Bayesian_Networks_15-49.txt 11.4 KB
Lectures/Week 1 - 04 ML-class Octave Tutorial/06_Vectorization_13-48.txt 11.4 KB
Lectures/Week 3 - 09 Inference-Belief Propagation Part 1/02_Properties_of_Cluster_Graphs_15-00.txt 11.3 KB
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Lectures/Week 3 - 08 Inference-Variable Elimination/02_Overview-_MAP_Inference_09-42.srt 11.2 KB
Lectures/Week 6 - 14 Inference- Temporal Models and Wrap-up/02_Inference-_Summary_12-45.txt 11.2 KB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/06_Naive_Bayes_09-52.srt 11.1 KB
Lectures/Week 2 - 06 Markov Network Fundamentals/02_General_Gibbs_Distribution_15-52.txt 11.0 KB
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Lectures/Week 6 - 16 ML-class Revision/04_Model_Selection_and_Train_Validation_Test_Sets_12-03.txt 11.0 KB
Lectures/Week 1 - 04 ML-class Octave Tutorial/03_Computing_On_Data_13-15.txt 10.9 KB
Lectures/Week 7 - 19 Learning- Parameter Estimation in MNs/02_Maximum_Likelihood_for_Conditional_Random_Fields_13-24.txt 10.8 KB
Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/01_Maximum_Likelihood_Estimation_14-59.txt 10.6 KB
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Lectures/Week 9 - 21 Learning With Incomplete Data/04_EM_in_Practice_11-17.txt 10.3 KB
Lectures/Week 1 - 03 Template Models/03_Temporal_Models_-_HMMs_12-01.txt 10.3 KB
Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/04_Bayesian_Prediction_13-40.txt 10.2 KB
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Lectures/Week 2 - 05 Structured CPDs/01_Overview-_Structured_CPDs_08-00.srt 10.0 KB
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Lectures/Week 8 - 20 Structure Learning/03_BIC_and_Asymptotic_Consistency_11-26.txt 9.3 KB
Lectures/Week 2 - 06 Markov Network Fundamentals/01_Pairwise_Markov_Networks_10-59.txt 9.3 KB
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