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Coursera - Probabilistic Graphical Models

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E74F08F0FC699E84A9EB046309727D07D80171C5

Number of Files:

547

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2016-06-19

Keywords:

Coursera Probabilistic Graphical Models

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File Name

Size

Assignments/Assignment 1/Assignment 1.pdf

276.03KB

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601B

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Assignments/Assignment 1/ConvertNetwork.m

3.76KB

Assignments/Assignment 1/Credit_net.net

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Assignments/Assignment 1/FactorMarginalization.m

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Lectures/Week 1 - 01 Introduction and Overview/02_Overview_and_Motivation_19-17.mp4

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Lectures/Week 1 - 01 Introduction and Overview/03_Distributions_04-56.mp4

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Lectures/Week 1 - 01 Introduction and Overview/04_Factors_06-40.mp4

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Lectures/Week 1 - 02 Bayesian Network Fundamentals/01_Semantics__Factorization_17-20.mp4

19.55MB

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21.13KB

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Lectures/Week 1 - 02 Bayesian Network Fundamentals/02_Reasoning_Patterns_09-59.mp4

10.78MB

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11.87KB

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Lectures/Week 1 - 02 Bayesian Network Fundamentals/03_Flow_of_Probabilistic_Influence_14-36.mp4

15.46MB

Lectures/Week 1 - 02 Bayesian Network Fundamentals/03_Flow_of_Probabilistic_Influence_14-36.srt

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Lectures/Week 1 - 02 Bayesian Network Fundamentals/03_Flow_of_Probabilistic_Influence_14-36.txt

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Lectures/Week 1 - 02 Bayesian Network Fundamentals/04_Conditional_Independence_12-38.mp4

15.51MB

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14.98KB

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Lectures/Week 1 - 02 Bayesian Network Fundamentals/05_Independencies_in_Bayesian_Networks_18-18.mp4

21.54MB

Lectures/Week 1 - 02 Bayesian Network Fundamentals/05_Independencies_in_Bayesian_Networks_18-18.srt

22.93KB

Lectures/Week 1 - 02 Bayesian Network Fundamentals/05_Independencies_in_Bayesian_Networks_18-18.txt

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Lectures/Week 1 - 02 Bayesian Network Fundamentals/06_Naive_Bayes_09-52.mp4

10.63MB

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11.13KB

Lectures/Week 1 - 02 Bayesian Network Fundamentals/06_Naive_Bayes_09-52.txt

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Lectures/Week 1 - 02 Bayesian Network Fundamentals/07_Application_-_Medical_Diagnosis_09-19.mp4

11.51MB

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Lectures/Week 1 - 02 Bayesian Network Fundamentals/08_Knowledge_Engineering_Example_-_SAMIAM_14-14.mp4

12.76MB

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Lectures/Week 1 - 02 Bayesian Network Fundamentals/08_Knowledge_Engineering_Example_-_SAMIAM_14-14.txt

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Lectures/Week 1 - 03 Template Models/01_Overview_of_Template_Models_10-55.mp4

11.57MB

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Lectures/Week 1 - 03 Template Models/02_Temporal_Models_-_DBNs_23-02.mp4

26.06MB

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Lectures/Week 1 - 03 Template Models/03_Temporal_Models_-_HMMs_12-01.mp4

13.58MB

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Lectures/Week 1 - 03 Template Models/04_Plate_Models_20-08.mp4

22.48MB

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Lectures/Week 1 - 04 ML-class Octave Tutorial/01_Basic_Operations_13-59.mp4

17.72MB

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Lectures/Week 1 - 04 ML-class Octave Tutorial/02_Moving_Data_Around_16-07.mp4

20.77MB

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Lectures/Week 1 - 04 ML-class Octave Tutorial/03_Computing_On_Data_13-15.mp4

15.25MB

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Lectures/Week 1 - 04 ML-class Octave Tutorial/04_Plotting_Data_09-38.mp4

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Lectures/Week 1 - 04 ML-class Octave Tutorial/05_Control_Statements-_for_while_if_statements_12-55.mp4

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Lectures/Week 1 - 04 ML-class Octave Tutorial/06_Vectorization_13-48.mp4

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Lectures/Week 1 - 04 ML-class Octave Tutorial/07_Working_on_and_Submitting_Programming_Exercises_03-33.mp4

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Lectures/Week 2 - 05 Structured CPDs/01_Overview-_Structured_CPDs_08-00.mp4

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Lectures/Week 2 - 05 Structured CPDs/02_Tree-Structured_CPDs_14-37.mp4

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Lectures/Week 2 - 05 Structured CPDs/03_Independence_of_Causal_Influence_13-08.mp4

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Lectures/Week 2 - 05 Structured CPDs/04_Continuous_Variables_13-25.mp4

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Lectures/Week 2 - 05 Structured CPDs/04_Continuous_Variables_13-25.srt

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Lectures/Week 2 - 06 Markov Network Fundamentals/01_Pairwise_Markov_Networks_10-59.mp4

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Lectures/Week 2 - 06 Markov Network Fundamentals/01_Pairwise_Markov_Networks_10-59.srt

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Lectures/Week 2 - 06 Markov Network Fundamentals/02_General_Gibbs_Distribution_15-52.mp4

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Lectures/Week 2 - 06 Markov Network Fundamentals/02_General_Gibbs_Distribution_15-52.srt

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Lectures/Week 2 - 06 Markov Network Fundamentals/02_General_Gibbs_Distribution_15-52.txt

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Lectures/Week 2 - 06 Markov Network Fundamentals/03_Conditional_Random_Fields_22-22.mp4

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Lectures/Week 2 - 06 Markov Network Fundamentals/03_Conditional_Random_Fields_22-22.srt

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Lectures/Week 2 - 06 Markov Network Fundamentals/04_Independencies_in_Markov_Networks_04-48.mp4

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Lectures/Week 2 - 06 Markov Network Fundamentals/04_Independencies_in_Markov_Networks_04-48.txt

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Lectures/Week 2 - 06 Markov Network Fundamentals/05_I-maps_and_perfect_maps_20-59.mp4

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Lectures/Week 2 - 06 Markov Network Fundamentals/05_I-maps_and_perfect_maps_20-59.srt

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Lectures/Week 2 - 06 Markov Network Fundamentals/05_I-maps_and_perfect_maps_20-59.txt

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Lectures/Week 2 - 06 Markov Network Fundamentals/06_Log-Linear_Models_22-08.mp4

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Lectures/Week 2 - 06 Markov Network Fundamentals/07_Shared_Features_in_Log-Linear_Models_08-28.mp4

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Lectures/Week 2 - 06 Markov Network Fundamentals/07_Shared_Features_in_Log-Linear_Models_08-28.srt

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Lectures/Week 3 - 07 Representation Wrapup-Knowledge Engineering/01_Knowledge_Engineering_23-05.mp4

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Lectures/Week 3 - 08 Inference-Variable Elimination/01_Overview-_Conditional_Probability_Queries_15-22.mp4

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Lectures/Week 3 - 08 Inference-Variable Elimination/01_Overview-_Conditional_Probability_Queries_15-22.srt

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Lectures/Week 3 - 08 Inference-Variable Elimination/02_Overview-_MAP_Inference_09-42.mp4

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Lectures/Week 3 - 08 Inference-Variable Elimination/03_Variable_Elimination_Algorithm_16-17.mp4

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Lectures/Week 3 - 08 Inference-Variable Elimination/04_Complexity_of_Variable_Elimination_12-48.mp4

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Lectures/Week 3 - 08 Inference-Variable Elimination/04_Complexity_of_Variable_Elimination_12-48.srt

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Lectures/Week 3 - 08 Inference-Variable Elimination/05_Graph-Based_Perspective_on_Variable_Elimination_15-25.mp4

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Lectures/Week 3 - 08 Inference-Variable Elimination/06_Finding_Elimination_Orderings_11-58.mp4

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Lectures/Week 3 - 09 Inference-Belief Propagation Part 1/01_Belief_Propagation_21-21.mp4

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Lectures/Week 3 - 09 Inference-Belief Propagation Part 1/02_Properties_of_Cluster_Graphs_15-00.mp4

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Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/01_Properties_of_Belief_Propagation_9-31.mp4

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Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/02_Clique_Tree_Algorithm_-_Correctness_18-23.mp4

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Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/03_Clique_Tree_Algorithm_-_Computation_16-18.mp4

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Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/04_Clique_Trees_and_Independence_15-21.mp4

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Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/05_Clique_Trees_and_VE_16-17.mp4

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Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/06_BP_In_Practice_15-38.mp4

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Lectures/Week 4 - 10 Inference-Belief Propagation Part 2/07_Loopy_BP_and_Message_Decoding_21-42.mp4

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Lectures/Week 4 - 11 Inference-MAP Estimation Part 1/01_Max_Sum_Message_Passing_20-27.mp4

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Lectures/Week 4 - 11 Inference-MAP Estimation Part 1/02_Finding_a_MAP_Assignment_3-57.mp4

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Lectures/Week 5 - 12 Inference- MAP Estimation Part 2/01_Tractable_MAP_Problems_15-04.mp4

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Lectures/Week 5 - 12 Inference- MAP Estimation Part 2/02_Dual_Decomposition_-_Intuition_17-46.mp4

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Lectures/Week 5 - 12 Inference- MAP Estimation Part 2/02_Dual_Decomposition_-_Intuition_17-46.srt

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Lectures/Week 5 - 12 Inference- MAP Estimation Part 2/03_Dual_Decomposition_-_Algorithm_16-16.mp4

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Lectures/Week 5 - 13 Inference- Sampling Methods/01_Simple_Sampling_23-37.mp4

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Lectures/Week 5 - 13 Inference- Sampling Methods/02_Markov_Chain_Monte_Carlo_14-18.mp4

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Lectures/Week 5 - 13 Inference- Sampling Methods/03_Using_a_Markov_Chain_15-27.mp4

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Lectures/Week 5 - 13 Inference- Sampling Methods/04_Gibbs_Sampling_19-26.mp4

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Lectures/Week 5 - 13 Inference- Sampling Methods/05_Metropolis_Hastings_Algorithm_27-06.mp4

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Lectures/Week 6 - 14 Inference- Temporal Models and Wrap-up/01_Inference_in_Temporal_Models_19-43.mp4

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Lectures/Week 6 - 14 Inference- Temporal Models and Wrap-up/02_Inference-_Summary_12-45.mp4

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Lectures/Week 6 - 15 Decision Theory/01_Maximum_Expected_Utility_25-57.mp4

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Lectures/Week 6 - 15 Decision Theory/02_Utility_Functions_18-15.mp4

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Lectures/Week 6 - 15 Decision Theory/03_Value_of_Perfect_Information_17-14.mp4

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Lectures/Week 6 - 16 ML-class Revision/01_Regularization-_The_Problem_of_Overfitting_09-42.mp4

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Lectures/Week 6 - 16 ML-class Revision/02_Regularization-_Cost_Function_10-10.mp4

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Lectures/Week 6 - 16 ML-class Revision/03_Evaluating_a_Hypothesis_07-35.mp4

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Lectures/Week 6 - 16 ML-class Revision/04_Model_Selection_and_Train_Validation_Test_Sets_12-03.mp4

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Lectures/Week 6 - 16 ML-class Revision/05_Diagnosing_Bias_vs_Variance_07-42.mp4

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Lectures/Week 6 - 16 ML-class Revision/06_Regularization_and_Bias_Variance_11-20.mp4

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Lectures/Week 6 - 17 Learning-Overview/01_Learning-_Overview_15-35.mp4

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Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/01_Maximum_Likelihood_Estimation_14-59.mp4

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Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/02_Maximum_Likelihood_Estimation_for_Bayesian_Networks_15-49.mp4

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Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/03_Bayesian_Estimation_15-27.mp4

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Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/04_Bayesian_Prediction_13-40.mp4

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Lectures/Week 7 - 18 Learning- Parameter Estimation in BNs/05_Bayesian_Estimation_for_Bayesian_Networks_17-02.mp4

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Lectures/Week 7 - 19 Learning- Parameter Estimation in MNs/01_Maximum_Likelihood_for_Log-Linear_Models_28-47.mp4

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Lectures/Week 7 - 19 Learning- Parameter Estimation in MNs/02_Maximum_Likelihood_for_Conditional_Random_Fields_13-24.mp4

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Lectures/Week 7 - 19 Learning- Parameter Estimation in MNs/03_MAP_Estimation_for_MRFs_and_CRFs_9-59.mp4

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Lectures/Week 8 - 20 Structure Learning/01_Structure_Learning_Overview_5-49.mp4

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Lectures/Week 8 - 20 Structure Learning/02_Likelihood_Scores_16-49.mp4

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Lectures/Week 8 - 20 Structure Learning/03_BIC_and_Asymptotic_Consistency_11-26.mp4

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Lectures/Week 8 - 20 Structure Learning/04_Bayesian_Scores_20-35.mp4

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Lectures/Week 8 - 20 Structure Learning/05_Learning_Tree_Structured_Networks_12-05.mp4

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Lectures/Week 8 - 20 Structure Learning/06_Learning_General_Graphs-_Heuristic_Search_23-36.mp4

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Lectures/Week 8 - 20 Structure Learning/07_Learning_General_Graphs-_Search_and_Decomposability_15-46.mp4

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Lectures/Week 9 - 21 Learning With Incomplete Data/01_Learning_With_Incomplete_Data_-_Overview_21-34.mp4

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Lectures/Week 9 - 21 Learning With Incomplete Data/02_Expectation_Maximization_-_Intro_16-17.mp4

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Lectures/Week 9 - 21 Learning With Incomplete Data/02_Expectation_Maximization_-_Intro_16-17.srt

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Lectures/Week 9 - 21 Learning With Incomplete Data/02_Expectation_Maximization_-_Intro_16-17.txt

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Lectures/Week 9 - 21 Learning With Incomplete Data/03_Analysis_of_EM_Algorithm_11-32.mp4

12.88MB

Lectures/Week 9 - 21 Learning With Incomplete Data/03_Analysis_of_EM_Algorithm_11-32.srt

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Lectures/Week 9 - 21 Learning With Incomplete Data/03_Analysis_of_EM_Algorithm_11-32.txt

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Lectures/Week 9 - 21 Learning With Incomplete Data/04_EM_in_Practice_11-17.mp4

12.69MB

Lectures/Week 9 - 21 Learning With Incomplete Data/04_EM_in_Practice_11-17.srt

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Lectures/Week 9 - 21 Learning With Incomplete Data/04_EM_in_Practice_11-17.txt

10.33KB

Lectures/Week 9 - 21 Learning With Incomplete Data/05_Latent_Variables_22-00.mp4

26.7MB

Lectures/Week 9 - 21 Learning With Incomplete Data/05_Latent_Variables_22-00.srt

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Lectures/Week 9 - 21 Learning With Incomplete Data/05_Latent_Variables_22-00.txt

17.29KB

Lectures/Week 9 - 22 Learning- Wrapup/01_Summary-_Learning_20-11.mp4

25.69MB

Lectures/Week 9 - 23 Summary/01_Class_Summary_24-38.mp4

32.21MB

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