[Coursera] Probabilistic Graphical Models
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Coursera - Probabilistic Graphical Models (547 files)
Assignments/Assignment 1/Assignment 1.pdf282.65kB
Assignments/Assignment 1/AssignmentToIndex.m0.60kB
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Assignments/Assignment 3/AssignmentToIndex.m0.65kB
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Assignments/Assignment 3/PA3Data.mat14.77kB
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Assignments/Assignment 4/Assignment 4.pdf431.40kB
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Assignments/Assignment 5/Assignment 5.pdf535.01kB
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Assignments/Assignment 6/Assignment 6.pdf467.24kB
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Assignments/Assignment 7/PA7Description.pdf498.81kB
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Assignments/Assignment 7/Part2FullDataset.mat7.36kB
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Assignments/Assignment 7/Part2Sample.mat86.72kB
Assignments/Assignment 7/Part2Test.mat18.62kB
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Assignments/Assignment 8/ClassifyDataset.m0.75kB
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Assignments/Assignment 8/PA8Data.mat195.99kB
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Assignments/Assignment 9/AssignmentToIndex.m0.61kB
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Lectures/Week 1 - 01 Introduction and Overview/01_Welcome_05-35.mp47.45MB
Lectures/Week 1 - 01 Introduction and Overview/01_Welcome_05-35.srt10.31kB
Lectures/Week 1 - 01 Introduction and Overview/01_Welcome_05-35.txt6.20kB
Lectures/Week 1 - 01 Introduction and Overview/02_Overview_and_Motivation_19-17.mp424.11MB
Lectures/Week 1 - 01 Introduction and Overview/02_Overview_and_Motivation_19-17.srt25.30kB
Lectures/Week 1 - 01 Introduction and Overview/02_Overview_and_Motivation_19-17.txt17.24kB
Lectures/Week 1 - 01 Introduction and Overview/03_Distributions_04-56.mp46.07MB
Lectures/Week 1 - 01 Introduction and Overview/03_Distributions_04-56.srt7.05kB
Lectures/Week 1 - 01 Introduction and Overview/03_Distributions_04-56.txt4.84kB
Lectures/Week 1 - 01 Introduction and Overview/04_Factors_06-40.mp47.72MB
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Lectures/Week 1 - 01 Introduction and Overview/04_Factors_06-40.txt5.97kB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/01_Semantics__Factorization_17-20.mp420.50MB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/01_Semantics__Factorization_17-20.srt21.63kB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/01_Semantics__Factorization_17-20.txt14.79kB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/02_Reasoning_Patterns_09-59.mp411.31MB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/02_Reasoning_Patterns_09-59.srt12.15kB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/02_Reasoning_Patterns_09-59.txt8.31kB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/03_Flow_of_Probabilistic_Influence_14-36.mp416.21MB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/03_Flow_of_Probabilistic_Influence_14-36.srt15.83kB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/03_Flow_of_Probabilistic_Influence_14-36.txt10.85kB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/04_Conditional_Independence_12-38.mp416.26MB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/04_Conditional_Independence_12-38.srt15.34kB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/04_Conditional_Independence_12-38.txt10.48kB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/05_Independencies_in_Bayesian_Networks_18-18.mp422.58MB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/05_Independencies_in_Bayesian_Networks_18-18.srt23.48kB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/05_Independencies_in_Bayesian_Networks_18-18.txt16.03kB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/06_Naive_Bayes_09-52.mp411.14MB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/06_Naive_Bayes_09-52.srt11.40kB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/06_Naive_Bayes_09-52.txt7.82kB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/07_Application_-_Medical_Diagnosis_09-19.mp412.07MB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/07_Application_-_Medical_Diagnosis_09-19.srt12.35kB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/07_Application_-_Medical_Diagnosis_09-19.txt8.47kB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/08_Knowledge_Engineering_Example_-_SAMIAM_14-14.mp413.38MB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/08_Knowledge_Engineering_Example_-_SAMIAM_14-14.srt23.59kB
Lectures/Week 1 - 02 Bayesian Network Fundamentals/08_Knowledge_Engineering_Example_-_SAMIAM_14-14.txt12.79kB
Lectures/Week 1 - 03 Template Models/01_Overview_of_Template_Models_10-55.mp412.13MB
Lectures/Week 1 - 03 Template Models/01_Overview_of_Template_Models_10-55.srt12.99kB
Lectures/Week 1 - 03 Template Models/01_Overview_of_Template_Models_10-55.txt8.88kB
Lectures/Week 1 - 03 Template Models/02_Temporal_Models_-_DBNs_23-02.mp427.33MB
Lectures/Week 1 - 03 Template Models/02_Temporal_Models_-_DBNs_23-02.srt26.97kB
Lectures/Week 1 - 03 Template Models/02_Temporal_Models_-_DBNs_23-02.txt18.45kB
Lectures/Week 1 - 03 Template Models/03_Temporal_Models_-_HMMs_12-01.mp414.24MB
Lectures/Week 1 - 03 Template Models/03_Temporal_Models_-_HMMs_12-01.srt15.47kB
Lectures/Week 1 - 03 Template Models/03_Temporal_Models_-_HMMs_12-01.txt10.57kB
Lectures/Week 1 - 03 Template Models/04_Plate_Models_20-08.mp423.57MB
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Lectures/Week 1 - 03 Template Models/04_Plate_Models_20-08.txt16.40kB
Lectures/Week 1 - 04 ML-class Octave Tutorial/01_Basic_Operations_13-59.mp418.58MB
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Lectures/Week 1 - 04 ML-class Octave Tutorial/02_Moving_Data_Around_16-07.mp421.78MB
Lectures/Week 1 - 04 ML-class Octave Tutorial/02_Moving_Data_Around_16-07.srt19.02kB
Lectures/Week 1 - 04 ML-class Octave Tutorial/02_Moving_Data_Around_16-07.txt13.05kB
Lectures/Week 1 - 04 ML-class Octave Tutorial/03_Computing_On_Data_13-15.mp415.99MB
Lectures/Week 1 - 04 ML-class Octave Tutorial/03_Computing_On_Data_13-15.srt16.30kB
Lectures/Week 1 - 04 ML-class Octave Tutorial/03_Computing_On_Data_13-15.txt11.20kB
Lectures/Week 1 - 04 ML-class Octave Tutorial/04_Plotting_Data_09-38.mp413.97MB
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Lectures/Week 1 - 04 ML-class Octave Tutorial/04_Plotting_Data_09-38.txt7.91kB
Lectures/Week 1 - 04 ML-class Octave Tutorial/05_Control_Statements-_for_while_if_statements_12-55.mp417.29MB
Lectures/Week 1 - 04 ML-class Octave Tutorial/05_Control_Statements-_for_while_if_statements_12-55.srt15.45kB
Lectures/Week 1 - 04 ML-class Octave Tutorial/05_Control_Statements-_for_while_if_statements_12-55.txt10.62kB
Lectures/Week 1 - 04 ML-class Octave Tutorial/06_Vectorization_13-48.mp416.87MB
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Lectures/Week 1 - 04 ML-class Octave Tutorial/07_Working_on_and_Submitting_Programming_Exercises_03-33.mp45.73MB
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Lectures/Week 1 - 04 ML-class Octave Tutorial/07_Working_on_and_Submitting_Programming_Exercises_03-33.txt3.19kB
Lectures/Week 2 - 05 Structured CPDs/01_Overview-_Structured_CPDs_08-00.mp410.12MB
Lectures/Week 2 - 05 Structured CPDs/01_Overview-_Structured_CPDs_08-00.srt10.22kB
Lectures/Week 2 - 05 Structured CPDs/01_Overview-_Structured_CPDs_08-00.txt6.98kB
Lectures/Week 2 - 05 Structured CPDs/02_Tree-Structured_CPDs_14-37.mp416.81MB
Lectures/Week 2 - 05 Structured CPDs/02_Tree-Structured_CPDs_14-37.srt17.21kB
Lectures/Week 2 - 05 Structured CPDs/02_Tree-Structured_CPDs_14-37.txt11.77kB
Lectures/Week 2 - 05 Structured CPDs/03_Independence_of_Causal_Influence_13-08.mp416.62MB
Lectures/Week 2 - 05 Structured CPDs/03_Independence_of_Causal_Influence_13-08.srt14.23kB
Lectures/Week 2 - 05 Structured CPDs/03_Independence_of_Causal_Influence_13-08.txt9.74kB
Lectures/Week 2 - 05 Structured CPDs/04_Continuous_Variables_13-25.mp416.07MB
Lectures/Week 2 - 05 Structured CPDs/04_Continuous_Variables_13-25.srt14.90kB
Lectures/Week 2 - 05 Structured CPDs/04_Continuous_Variables_13-25.txt10.18kB
Lectures/Week 2 - 06 Markov Network Fundamentals/01_Pairwise_Markov_Networks_10-59.mp413.16MB
Lectures/Week 2 - 06 Markov Network Fundamentals/01_Pairwise_Markov_Networks_10-59.srt13.82kB
Lectures/Week 2 - 06 Markov Network Fundamentals/01_Pairwise_Markov_Networks_10-59.txt9.48kB
Lectures/Week 2 - 06 Markov Network Fundamentals/02_General_Gibbs_Distribution_15-52.mp419.85MB
Lectures/Week 2 - 06 Markov Network Fundamentals/02_General_Gibbs_Distribution_15-52.srt16.60kB
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Lectures/Week 2 - 06 Markov Network Fundamentals/03_Conditional_Random_Fields_22-22.mp426.27MB
Lectures/Week 2 - 06 Markov Network Fundamentals/03_Conditional_Random_Fields_22-22.srt23.96kB
Lectures/Week 2 - 06 Markov Network Fundamentals/03_Conditional_Random_Fields_22-22.txt16.39kB
Lectures/Week 2 - 06 Markov Network Fundamentals/04_Independencies_in_Markov_Networks_04-48.mp46.12MB
Lectures/Week 2 - 06 Markov Network Fundamentals/04_Independencies_in_Markov_Networks_04-48.srt5.49kB
Lectures/Week 2 - 06 Markov Network Fundamentals/04_Independencies_in_Markov_Networks_04-48.txt3.76kB
Lectures/Week 2 - 06 Markov Network Fundamentals/05_I-maps_and_perfect_maps_20-59.mp423.48MB
Lectures/Week 2 - 06 Markov Network Fundamentals/05_I-maps_and_perfect_maps_20-59.srt22.86kB
Lectures/Week 2 - 06 Markov Network Fundamentals/05_I-maps_and_perfect_maps_20-59.txt15.56kB
Lectures/Week 2 - 06 Markov Network Fundamentals/06_Log-Linear_Models_22-08.mp427.01MB
Lectures/Week 2 - 06 Markov Network Fundamentals/06_Log-Linear_Models_22-08.srt27.39kB
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Lectures/Week 2 - 06 Markov Network Fundamentals/07_Shared_Features_in_Log-Linear_Models_08-28.mp410.50MB
Lectures/Week 2 - 06 Markov Network Fundamentals/07_Shared_Features_in_Log-Linear_Models_08-28.srt9.24kB
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Lectures/Week 3 - 07 Representation Wrapup-Knowledge Engineering/01_Knowledge_Engineering_23-05.mp425.84MB
Lectures/Week 3 - 07 Representation Wrapup-Knowledge Engineering/01_Knowledge_Engineering_23-05.srt28.87kB
Lectures/Week 3 - 07 Representation Wrapup-Knowledge Engineering/01_Knowledge_Engineering_23-05.txt18.72kB
Lectures/Week 3 - 08 Inference-Variable Elimination/01_Overview-_Conditional_Probability_Queries_15-22.mp49.45MB
Lectures/Week 3 - 08 Inference-Variable Elimination/01_Overview-_Conditional_Probability_Queries_15-22.srt17.86kB
Lectures/Week 3 - 08 Inference-Variable Elimination/01_Overview-_Conditional_Probability_Queries_15-22.txt12.19kB
Lectures/Week 3 - 08 Inference-Variable Elimination/02_Overview-_MAP_Inference_09-42.mp46.16MB
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Lectures/Week 3 - 08 Inference-Variable Elimination/03_Variable_Elimination_Algorithm_16-17.mp411.64MB
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Lectures/Week 3 - 08 Inference-Variable Elimination/04_Complexity_of_Variable_Elimination_12-48.mp415.42MB
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Lectures/Week 3 - 08 Inference-Variable Elimination/05_Graph-Based_Perspective_on_Variable_Elimination_15-25.mp410.01MB
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Lectures/Week 3 - 08 Inference-Variable Elimination/06_Finding_Elimination_Orderings_11-58.mp49.20MB
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Lectures/Week 3 - 09 Inference-Belief Propagation Part 1/01_Belief_Propagation_21-21.mp413.90MB
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Lectures/Week 3 - 09 Inference-Belief Propagation Part 1/02_Properties_of_Cluster_Graphs_15-00.mp410.20MB
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Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/01_Properties_of_Belief_Propagation_9-31.mp46.03MB
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Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/02_Clique_Tree_Algorithm_-_Correctness_18-23.mp410.99MB
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Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/03_Clique_Tree_Algorithm_-_Computation_16-18.mp49.14MB
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Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/04_Clique_Trees_and_Independence_15-21.mp49.99MB
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Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/05_Clique_Trees_and_VE_16-17.mp411.07MB
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Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/06_BP_In_Practice_15-38.mp49.65MB
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Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/07_Loopy_BP_and_Message_Decoding_21-42.mp413.79MB
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Lectures/Week 4 - 11 Inference-MAP Estimation Part 1/01_Max_Sum_Message_Passing_20-27.mp413.26MB
Lectures/Week 4 - 11 Inference-MAP Estimation Part 1/01_Max_Sum_Message_Passing_20-27.srt22.79kB
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Lectures/Week 4 - 11 Inference-MAP Estimation Part 1/02_Finding_a_MAP_Assignment_3-57.mp42.80MB
Lectures/Week 4 - 11 Inference-MAP Estimation Part 1/02_Finding_a_MAP_Assignment_3-57.srt5.24kB
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Lectures/Week 5 - 12 Inference- MAP Estimation Part 2/01_Tractable_MAP_Problems_15-04.mp410.16MB
Lectures/Week 5 - 12 Inference- MAP Estimation Part 2/01_Tractable_MAP_Problems_15-04.srt19.39kB
Lectures/Week 5 - 12 Inference- MAP Estimation Part 2/01_Tractable_MAP_Problems_15-04.txt13.23kB
Lectures/Week 5 - 12 Inference- MAP Estimation Part 2/02_Dual_Decomposition_-_Intuition_17-46.mp411.74MB
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Lectures/Week 5 - 12 Inference- MAP Estimation Part 2/03_Dual_Decomposition_-_Algorithm_16-16.mp410.21MB
Lectures/Week 5 - 12 Inference- MAP Estimation Part 2/03_Dual_Decomposition_-_Algorithm_16-16.srt18.89kB
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Lectures/Week 5 - 13 Inference- Sampling Methods/01_Simple_Sampling_23-37.mp414.45MB
Lectures/Week 5 - 13 Inference- Sampling Methods/01_Simple_Sampling_23-37.srt26.84kB
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Lectures/Week 5 - 13 Inference- Sampling Methods/02_Markov_Chain_Monte_Carlo_14-18.mp49.66MB
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Lectures/Week 5 - 13 Inference- Sampling Methods/03_Using_a_Markov_Chain_15-27.mp49.99MB
Lectures/Week 5 - 13 Inference- Sampling Methods/03_Using_a_Markov_Chain_15-27.srt18.31kB
Lectures/Week 5 - 13 Inference- Sampling Methods/03_Using_a_Markov_Chain_15-27.txt12.53kB
Lectures/Week 5 - 13 Inference- Sampling Methods/04_Gibbs_Sampling_19-26.mp413.11MB
Lectures/Week 5 - 13 Inference- Sampling Methods/04_Gibbs_Sampling_19-26.srt20.03kB
Lectures/Week 5 - 13 Inference- Sampling Methods/04_Gibbs_Sampling_19-26.txt13.72kB
Lectures/Week 5 - 13 Inference- Sampling Methods/05_Metropolis_Hastings_Algorithm_27-06.mp417.73MB
Lectures/Week 5 - 13 Inference- Sampling Methods/05_Metropolis_Hastings_Algorithm_27-06.srt33.24kB
Lectures/Week 5 - 13 Inference- Sampling Methods/05_Metropolis_Hastings_Algorithm_27-06.txt22.71kB
Lectures/Week 6 - 14 Inference- Temporal Models and Wrap-up/01_Inference_in_Temporal_Models_19-43.mp424.44MB
Lectures/Week 6 - 14 Inference- Temporal Models and Wrap-up/01_Inference_in_Temporal_Models_19-43.srt25.35kB
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Lectures/Week 6 - 14 Inference- Temporal Models and Wrap-up/02_Inference-_Summary_12-45.mp414.84MB
Lectures/Week 6 - 14 Inference- Temporal Models and Wrap-up/02_Inference-_Summary_12-45.srt16.75kB
Lectures/Week 6 - 14 Inference- Temporal Models and Wrap-up/02_Inference-_Summary_12-45.txt11.42kB
Lectures/Week 6 - 15 Decision Theory/01_Maximum_Expected_Utility_25-57.mp430.40MB
Lectures/Week 6 - 15 Decision Theory/01_Maximum_Expected_Utility_25-57.srt30.57kB
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Lectures/Week 6 - 15 Decision Theory/02_Utility_Functions_18-15.mp420.64MB
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Lectures/Week 6 - 15 Decision Theory/02_Utility_Functions_18-15.txt14.71kB
Lectures/Week 6 - 15 Decision Theory/03_Value_of_Perfect_Information_17-14.mp420.22MB
Lectures/Week 6 - 15 Decision Theory/03_Value_of_Perfect_Information_17-14.srt22.16kB
Lectures/Week 6 - 15 Decision Theory/03_Value_of_Perfect_Information_17-14.txt15.31kB
Lectures/Week 6 - 16 ML-class Revision/01_Regularization-_The_Problem_of_Overfitting_09-42.mp411.69MB
Lectures/Week 6 - 16 ML-class Revision/01_Regularization-_The_Problem_of_Overfitting_09-42.srt13.22kB
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Lectures/Week 6 - 16 ML-class Revision/02_Regularization-_Cost_Function_10-10.mp412.19MB
Lectures/Week 6 - 16 ML-class Revision/02_Regularization-_Cost_Function_10-10.srt13.10kB
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Lectures/Week 6 - 16 ML-class Revision/03_Evaluating_a_Hypothesis_07-35.mp48.90MB
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Lectures/Week 6 - 16 ML-class Revision/04_Model_Selection_and_Train_Validation_Test_Sets_12-03.mp414.75MB
Lectures/Week 6 - 16 ML-class Revision/04_Model_Selection_and_Train_Validation_Test_Sets_12-03.srt16.42kB
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Lectures/Week 6 - 16 ML-class Revision/05_Diagnosing_Bias_vs_Variance_07-42.mp49.41MB
Lectures/Week 6 - 16 ML-class Revision/05_Diagnosing_Bias_vs_Variance_07-42.srt10.69kB
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Lectures/Week 6 - 16 ML-class Revision/06_Regularization_and_Bias_Variance_11-20.mp413.21MB
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Lectures/Week 6 - 17 Learning-Overview/01_Learning-_Overview_15-35.mp418.36MB
Lectures/Week 6 - 17 Learning-Overview/01_Learning-_Overview_15-35.srt19.96kB
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Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/01_Maximum_Likelihood_Estimation_14-59.mp415.88MB
Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/01_Maximum_Likelihood_Estimation_14-59.srt15.77kB
Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/01_Maximum_Likelihood_Estimation_14-59.txt10.86kB
Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/02_Maximum_Likelihood_Estimation_for_Bayesian_Networks_15-49.mp418.58MB
Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/02_Maximum_Likelihood_Estimation_for_Bayesian_Networks_15-49.srt17.15kB
Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/02_Maximum_Likelihood_Estimation_for_Bayesian_Networks_15-49.txt11.71kB
Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/03_Bayesian_Estimation_15-27.mp419.57MB
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Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/04_Bayesian_Prediction_13-40.mp417.00MB
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Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/05_Bayesian_Estimation_for_Bayesian_Networks_17-02.mp422.18MB
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Lectures/Week 7 - 19 Learning- Parameter Estimation in MNs/01_Maximum_Likelihood_for_Log-Linear_Models_28-47.mp436.28MB
Lectures/Week 7 - 19 Learning- Parameter Estimation in MNs/01_Maximum_Likelihood_for_Log-Linear_Models_28-47.srt31.67kB
Lectures/Week 7 - 19 Learning- Parameter Estimation in MNs/01_Maximum_Likelihood_for_Log-Linear_Models_28-47.txt21.64kB
Lectures/Week 7 - 19 Learning- Parameter Estimation in MNs/02_Maximum_Likelihood_for_Conditional_Random_Fields_13-24.mp415.83MB
Lectures/Week 7 - 19 Learning- Parameter Estimation in MNs/02_Maximum_Likelihood_for_Conditional_Random_Fields_13-24.srt16.14kB
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Lectures/Week 7 - 19 Learning- Parameter Estimation in MNs/03_MAP_Estimation_for_MRFs_and_CRFs_9-59.mp411.84MB
Lectures/Week 7 - 19 Learning- Parameter Estimation in MNs/03_MAP_Estimation_for_MRFs_and_CRFs_9-59.srt12.67kB
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Lectures/Week 8 - 20 Structure Learning/01_Structure_Learning_Overview_5-49.mp46.98MB
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Lectures/Week 8 - 20 Structure Learning/02_Likelihood_Scores_16-49.mp419.64MB
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Lectures/Week 8 - 20 Structure Learning/03_BIC_and_Asymptotic_Consistency_11-26.mp413.14MB
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Lectures/Week 8 - 20 Structure Learning/04_Bayesian_Scores_20-35.mp423.72MB
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Lectures/Week 8 - 20 Structure Learning/05_Learning_Tree_Structured_Networks_12-05.mp415.16MB
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Lectures/Week 8 - 20 Structure Learning/06_Learning_General_Graphs-_Heuristic_Search_23-36.mp428.07MB
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Lectures/Week 8 - 20 Structure Learning/07_Learning_General_Graphs-_Search_and_Decomposability_15-46.mp418.49MB
Lectures/Week 9 - 21 Learning With Incomplete Data/01_Learning_With_Incomplete_Data_-_Overview_21-34.mp426.07MB
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Lectures/Week 9 - 21 Learning With Incomplete Data/01_Learning_With_Incomplete_Data_-_Overview_21-34.txt17.23kB
Lectures/Week 9 - 21 Learning With Incomplete Data/02_Expectation_Maximization_-_Intro_16-17.mp418.94MB
Lectures/Week 9 - 21 Learning With Incomplete Data/02_Expectation_Maximization_-_Intro_16-17.srt20.53kB
Lectures/Week 9 - 21 Learning With Incomplete Data/02_Expectation_Maximization_-_Intro_16-17.txt14.07kB
Lectures/Week 9 - 21 Learning With Incomplete Data/03_Analysis_of_EM_Algorithm_11-32.mp413.51MB
Lectures/Week 9 - 21 Learning With Incomplete Data/03_Analysis_of_EM_Algorithm_11-32.srt13.43kB
Lectures/Week 9 - 21 Learning With Incomplete Data/03_Analysis_of_EM_Algorithm_11-32.txt9.21kB
Lectures/Week 9 - 21 Learning With Incomplete Data/04_EM_in_Practice_11-17.mp413.30MB
Lectures/Week 9 - 21 Learning With Incomplete Data/04_EM_in_Practice_11-17.srt15.49kB
Lectures/Week 9 - 21 Learning With Incomplete Data/04_EM_in_Practice_11-17.txt10.58kB
Lectures/Week 9 - 21 Learning With Incomplete Data/05_Latent_Variables_22-00.mp428.00MB
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Lectures/Week 9 - 22 Learning- Wrapup/01_Summary-_Learning_20-11.mp426.94MB
Lectures/Week 9 - 23 Summary/01_Class_Summary_24-38.mp433.78MB
Type: Course
Tags:

Bibtex:
@article{,
title= {[Coursera] Probabilistic Graphical Models},
keywords= {},
journal= {},
author= {Stanford University},
year= {2013},
url= {},
license= {},
abstract= {In this class, you will learn the basics of the PGM representation and how to construct them, using both human knowledge and machine learning techniques.




Uncertainty is unavoidable in real-world applications: we can almost never predict with certainty what will happen in the future, and even in the present and the past, many important aspects of the world are not observed with certainty. Probability theory gives us the basic foundation to model our beliefs about the different possible states of the world, and to update these beliefs as new evidence is obtained. These beliefs can be combined with individual preferences to help guide our actions, and even in selecting which observations to make. While probability theory has existed since the 17th century, our ability to use it effectively on large problems involving many inter-related variables is fairly recent, and is due largely to the development of a framework known as Probabilistic Graphical Models (PGMs). This framework, which spans methods such as Bayesian networks and Markov random fields, uses ideas from discrete data structures in computer science to efficiently encode and manipulate probability distributions over high-dimensional spaces, often involving hundreds or even many thousands of variables. These methods have been used in an enormous range of application domains, which include: web search, medical and fault diagnosis, image understanding, reconstruction of biological networks, speech recognition, natural language processing, decoding of messages sent over a noisy communication channel, robot navigation, and many more. The PGM framework provides an essential tool for anyone who wants to learn how to reason coherently from limited and noisy observations.},
superseded= {},
terms= {}
}