pgm

磁力链接/BT种子名称

pgm

磁力链接/BT种子简介

种子哈希：5648d60c0afcfd91c0987ec1891949d63f645dc6
文件大小： 1.36G
已经下载：4362次
下载速度：极快
收录时间：2017-02-08
最近下载：2025-08-31

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磁力链接下载

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文件列表

  • 19 - 1 - Maximum Likelihood for Log-Linear Models (28-47).mp4 36.3 MB
  • 23 - 1 - Class Summary (24-38).mp4 33.8 MB
  • 15 - 1 - Maximum Expected Utility (25-57).mp4 30.4 MB
  • 20 - 6 - Learning General Graphs- Heuristic Search (23-36).mp4 28.1 MB
  • 21 - 5 - Latent Variables (22-00).mp4 28.0 MB
  • 3 - 2 - Temporal Models - DBNs (23-02).mp4 27.3 MB
  • 6 - 6 - Log-Linear Models (22-08).mp4 27.0 MB
  • 22 - 1 - Summary- Learning (20-11).mp4 26.9 MB
  • 6 - 3 - Conditional Random Fields (22-22).mp4 26.3 MB
  • 21 - 1 - Learning With Incomplete Data - Overview (21-34).mp4 26.1 MB
  • 7 - 1 - Knowledge Engineering (23-05).mp4 25.9 MB
  • 1 - 2 - Overview and Motivation (19-17).mp4 24.1 MB
  • 20 - 4 - Bayesian Scores (20-35).mp4 23.7 MB
  • 3 - 4 - Plate Models (20-08).mp4 23.6 MB
  • 6 - 5 - I-maps and perfect maps (20-59).mp4 23.5 MB
  • 2 - 5 - Independencies in Bayesian Networks (18-18).mp4 22.6 MB
  • 18 - 5 - Bayesian Estimation for Bayesian Networks (17-02).mp4 22.2 MB
  • 4 - 2 - Moving Data Around (16-07).mp4 21.8 MB
  • 15 - 2 - Utility Functions (18-15).mp4 20.6 MB
  • 2 - 1 - Semantics & Factorization (17-20).mp4 20.5 MB
  • 15 - 3 - Value of Perfect Information (17-14).mp4 20.2 MB
  • 6 - 2 - General Gibbs Distribution (15-52).mp4 19.9 MB
  • 20 - 2 - Likelihood Scores (16-49).mp4 19.6 MB
  • 18 - 3 - Bayesian Estimation (15-27).mp4 19.6 MB
  • 21 - 2 - Expectation Maximization - Intro (16-17).mp4 18.9 MB
  • 18 - 2 - Maximum Likelihood Estimation for Bayesian Networks (15-49).mp4 18.6 MB
  • 4 - 1 - Basic Operations (13-59).mp4 18.6 MB
  • 20 - 7 - Learning General Graphs- Search and Decomposability (15-46).mp4 18.5 MB
  • 17 - 1 - Learning- Overview (15-35).mp4 18.4 MB
  • 13 - 5 - Metropolis Hastings Algorithm (27-06).mp4 17.7 MB
  • 4 - 5 - Control Statements- for, while, if statements (12-55).mp4 17.3 MB
  • 18 - 4 - Bayesian Prediction (13-40).mp4 17.0 MB
  • 4 - 6 - Vectorization (13-48).mp4 16.9 MB
  • 5 - 2 - Tree-Structured CPDs (14-37).mp4 16.8 MB
  • 5 - 3 - Independence of Causal Influence (13-08).mp4 16.6 MB
  • 2 - 4 - Conditional Independence (12-38).mp4 16.3 MB
  • 2 - 3 - Flow of Probabilistic Influence (14-36).mp4 16.2 MB
  • 5 - 4 - Continuous Variables (13-25).mp4 16.1 MB
  • 4 - 3 - Computing On Data (13-15).mp4 16.0 MB
  • 18 - 1 - Maximum Likelihood Estimation (14-59).mp4 15.9 MB
  • 19 - 2 - Maximum Likelihood for Conditional Random Fields (13-24).mp4 15.8 MB
  • 20 - 5 - Learning Tree Structured Networks (12-05).mp4 15.2 MB
  • 16 - 4 - Model Selection and Train Validation Test Sets (12-03).mp4 14.8 MB
  • 13 - 1 - Simple Sampling (23-37).mp4 14.4 MB
  • 3 - 3 - Temporal Models - HMMs (12-01).mp4 14.2 MB
  • 14 - 1 - Inference in Temporal Models (19-43).mp4 14.2 MB
  • 4 - 4 - Plotting Data (09-38).mp4 14.0 MB
  • 9 - 1 - Belief Propagation (21-21).mp4 13.9 MB
  • 10 - 7 - Loopy BP and Message Decoding (21-42).mp4 13.8 MB
  • 21 - 3 - Analysis of EM Algorithm (11-32).mp4 13.5 MB
  • 2 - 8 - Knowledge Engineering Example - SAMIAM (14-14).mp4 13.4 MB
  • 21 - 4 - EM in Practice (11-17).mp4 13.3 MB
  • 11 - 1 - Max Sum Message Passing (20-27).mp4 13.3 MB
  • 16 - 6 - Regularization and Bias Variance (11-20).mp4 13.2 MB
  • 6 - 1 - Pairwise Markov Networks (10-59).mp4 13.2 MB
  • 20 - 3 - BIC and Asymptotic Consistency (11-26).mp4 13.1 MB
  • 13 - 4 - Gibbs Sampling (19-26).mp4 13.1 MB
  • 16 - 2 - Regularization- Cost Function (10-10).mp4 12.2 MB
  • 3 - 1 - Overview of Template Models (10-55).mp4 12.1 MB
  • 2 - 7 - Application - Medical Diagnosis (09-19).mp4 12.1 MB
  • 19 - 3 - MAP Estimation for MRFs and CRFs (9-59).mp4 11.8 MB
  • 12 - 2 - Dual Decomposition - Intuition (17-46).mp4 11.7 MB
  • 16 - 1 - Regularization- The Problem of Overfitting (09-42).mp4 11.7 MB
  • 8 - 3 - Variable Elimination Algorithm (16-17).mp4 11.6 MB
  • 2 - 2 - Reasoning Patterns (09-59).mp4 11.3 MB
  • 2 - 6 - Naive Bayes (09-52).mp4 11.2 MB
  • 10 - 5 - Clique Trees and VE (16-17).mp4 11.1 MB
  • 10 - 2 - Clique Tree Algorithm - Correctness (18-23).mp4 11.0 MB
  • 6 - 7 - Shared Features in Log-Linear Models (08-28).mp4 10.5 MB
  • 12 - 3 - Dual Decomposition - Algorithm (16-16).mp4 10.2 MB
  • 9 - 2 - Properties of Cluster Graphs (15-00).mp4 10.2 MB
  • 12 - 1 - Tractable MAP Problems (15-04).mp4 10.2 MB
  • 5 - 1 - Overview- Structured CPDs (08-00).mp4 10.1 MB
  • 8 - 5 - Graph-Based Perspective on Variable Elimination (15-25).mp4 10.0 MB
  • 13 - 3 - Using a Markov Chain (15-27).mp4 10.0 MB
  • 10 - 4 - Clique Trees and Independence (15-21).mp4 10.0 MB
  • 13 - 2 - Markov Chain Monte Carlo (14-18).mp4 9.7 MB
  • 10 - 6 - BP In Practice (15-38).mp4 9.6 MB
  • 8 - 1 - Overview- Conditional Probability Queries (15-22).mp4 9.5 MB
  • 16 - 5 - Diagnosing Bias vs Variance (07-42).mp4 9.4 MB
  • 8 - 6 - Finding Elimination Orderings (11-58).mp4 9.2 MB
  • 10 - 3 - Clique Tree Algorithm - Computation (16-18).mp4 9.1 MB
  • 8 - 4 - Complexity of Variable Elimination (12-48).mp4 9.0 MB
  • 16 - 3 - Evaluating a Hypothesis (07-35).mp4 8.9 MB
  • 14 - 2 - Inference- Summary (12-45).mp4 8.2 MB
  • 1 - 4 - Factors (06-40).mp4 7.7 MB
  • 1 - 1 - Welcome! (05-35).mp4 7.5 MB
  • 20 - 1 - Structure Learning Overview (5-49).mp4 7.0 MB
  • 8 - 2 - Overview- MAP Inference (09-42).mp4 6.2 MB
  • 6 - 4 - Independencies in Markov Networks (04-48).mp4 6.1 MB
  • 1 - 3 - Distributions (04-56).mp4 6.1 MB
  • 10 - 1 - Properties of Belief Propagation (9-31).mp4 6.0 MB
  • 4 - 7 - Working on and Submitting Programming Exercises (03-33).mp4 5.8 MB
  • 11 - 2 - Finding a MAP Assignment (3-57).mp4 2.8 MB
  • 13 - 5 - Metropolis Hastings Algorithm (27-06).srt 33.2 kB
  • 19 - 1 - Maximum Likelihood for Log-Linear Models (28-47).srt 31.7 kB
  • 20 - 6 - Learning General Graphs- Heuristic Search (23-36).srt 31.0 kB
  • 15 - 1 - Maximum Expected Utility (25-57).srt 30.6 kB
  • 7 - 1 - Knowledge Engineering (23-05).srt 28.9 kB
  • 10 - 7 - Loopy BP and Message Decoding (21-42).srt 27.2 kB
  • 6 - 6 - Log-Linear Models (22-08).srt 27.2 kB
  • 3 - 2 - Temporal Models - DBNs (23-02).srt 27.0 kB
  • 13 - 1 - Simple Sampling (23-37).srt 26.9 kB
  • 21 - 5 - Latent Variables (22-00).srt 25.9 kB
  • 14 - 1 - Inference in Temporal Models (19-43).srt 25.4 kB
  • 1 - 2 - Overview and Motivation (19-17).srt 25.3 kB
  • 21 - 1 - Learning With Incomplete Data - Overview (21-34).srt 25.1 kB
  • 9 - 1 - Belief Propagation (21-21).srt 24.4 kB
  • 20 - 4 - Bayesian Scores (20-35).srt 24.4 kB
  • 6 - 3 - Conditional Random Fields (22-22).srt 24.0 kB
  • 3 - 4 - Plate Models (20-08).srt 23.9 kB
  • 2 - 8 - Knowledge Engineering Example - SAMIAM (14-14).srt 23.6 kB
  • 2 - 5 - Independencies in Bayesian Networks (18-18).srt 23.5 kB
  • 6 - 5 - I-maps and perfect maps (20-59).srt 23.1 kB
  • 11 - 1 - Max Sum Message Passing (20-27).srt 22.8 kB
  • 15 - 3 - Value of Perfect Information (17-14).srt 22.2 kB
  • 2 - 1 - Semantics & Factorization (17-20).srt 21.6 kB
  • 15 - 2 - Utility Functions (18-15).srt 21.5 kB
  • 10 - 2 - Clique Tree Algorithm - Correctness (18-23).srt 20.6 kB
  • 21 - 2 - Expectation Maximization - Intro (16-17).srt 20.5 kB
  • 12 - 2 - Dual Decomposition - Intuition (17-46).srt 20.1 kB
  • 13 - 4 - Gibbs Sampling (19-26).srt 20.0 kB
  • 17 - 1 - Learning- Overview (15-35).srt 19.9 kB
  • 20 - 7 - Learning General Graphs- Search and Decomposability (15-46).srt 19.4 kB
  • 12 - 1 - Tractable MAP Problems (15-04).srt 19.4 kB
  • 18 - 5 - Bayesian Estimation for Bayesian Networks (17-02).srt 19.4 kB
  • 20 - 2 - Likelihood Scores (16-49).srt 19.3 kB
  • 4 - 2 - Moving Data Around (16-07).srt 19.0 kB
  • 12 - 3 - Dual Decomposition - Algorithm (16-16).srt 19.0 kB
  • 13 - 3 - Using a Markov Chain (15-27).srt 18.3 kB
  • 18 - 3 - Bayesian Estimation (15-27).srt 18.2 kB
  • 10 - 5 - Clique Trees and VE (16-17).srt 18.1 kB
  • 8 - 3 - Variable Elimination Algorithm (16-17).srt 17.9 kB
  • 8 - 1 - Overview- Conditional Probability Queries (15-22).srt 17.9 kB
  • 10 - 6 - BP In Practice (15-38).srt 17.7 kB
  • 13 - 2 - Markov Chain Monte Carlo (14-18).srt 17.4 kB
  • 10 - 4 - Clique Trees and Independence (15-21).srt 17.3 kB
  • 5 - 2 - Tree-Structured CPDs (14-37).srt 17.2 kB
  • 18 - 2 - Maximum Likelihood Estimation for Bayesian Networks (15-49).srt 17.1 kB
  • 4 - 6 - Vectorization (13-48).srt 17.1 kB
  • 9 - 2 - Properties of Cluster Graphs (15-00).srt 16.9 kB
  • 4 - 1 - Basic Operations (13-59).srt 16.8 kB
  • 14 - 2 - Inference- Summary (12-45).srt 16.7 kB
  • 6 - 2 - General Gibbs Distribution (15-52).srt 16.7 kB
  • 10 - 3 - Clique Tree Algorithm - Computation (16-18).srt 16.5 kB
  • 16 - 4 - Model Selection and Train Validation Test Sets (12-03).srt 16.4 kB
  • 4 - 3 - Computing On Data (13-15).srt 16.3 kB
  • 19 - 2 - Maximum Likelihood for Conditional Random Fields (13-24).srt 16.1 kB
  • 2 - 3 - Flow of Probabilistic Influence (14-36).srt 15.8 kB
  • 18 - 1 - Maximum Likelihood Estimation (14-59).srt 15.8 kB
  • 21 - 4 - EM in Practice (11-17).srt 15.5 kB
  • 3 - 3 - Temporal Models - HMMs (12-01).srt 15.5 kB
  • 4 - 5 - Control Statements- for, while, if statements (12-55).srt 15.5 kB
  • 18 - 4 - Bayesian Prediction (13-40).srt 15.4 kB
  • 2 - 4 - Conditional Independence (12-38).srt 15.3 kB
  • 16 - 6 - Regularization and Bias Variance (11-20).srt 15.2 kB
  • 8 - 5 - Graph-Based Perspective on Variable Elimination (15-25).srt 15.2 kB
  • 5 - 4 - Continuous Variables (13-25).srt 14.9 kB
  • 8 - 6 - Finding Elimination Orderings (11-58).srt 14.4 kB
  • 20 - 5 - Learning Tree Structured Networks (12-05).srt 14.3 kB
  • 5 - 3 - Independence of Causal Influence (13-08).srt 14.2 kB
  • 20 - 3 - BIC and Asymptotic Consistency (11-26).srt 13.9 kB
  • 6 - 1 - Pairwise Markov Networks (10-59).srt 13.8 kB
  • 16 - 2 - Regularization- Cost Function (10-10).srt 13.6 kB
  • 16 - 1 - Regularization- The Problem of Overfitting (09-42).srt 13.5 kB
  • 21 - 3 - Analysis of EM Algorithm (11-32).srt 13.4 kB
  • 8 - 4 - Complexity of Variable Elimination (12-48).srt 13.2 kB
  • 3 - 1 - Overview of Template Models (10-55).srt 13.0 kB
  • 19 - 3 - MAP Estimation for MRFs and CRFs (9-59).srt 12.7 kB
  • 2 - 7 - Application - Medical Diagnosis (09-19).srt 12.4 kB
  • 2 - 2 - Reasoning Patterns (09-59).srt 12.2 kB
  • 4 - 4 - Plotting Data (09-38).srt 11.5 kB
  • 8 - 2 - Overview- MAP Inference (09-42).srt 11.4 kB
  • 2 - 6 - Naive Bayes (09-52).srt 11.4 kB
  • 10 - 1 - Properties of Belief Propagation (9-31).srt 10.7 kB
  • 16 - 5 - Diagnosing Bias vs Variance (07-42).srt 10.7 kB
  • 1 - 1 - Welcome! (05-35).srt 10.3 kB
  • 5 - 1 - Overview- Structured CPDs (08-00).srt 10.2 kB
  • 16 - 3 - Evaluating a Hypothesis (07-35).srt 9.3 kB
  • 6 - 7 - Shared Features in Log-Linear Models (08-28).srt 9.2 kB
  • 1 - 4 - Factors (06-40).srt 8.7 kB
  • 20 - 1 - Structure Learning Overview (5-49).srt 8.0 kB
  • 1 - 3 - Distributions (04-56).srt 7.1 kB
  • 6 - 4 - Independencies in Markov Networks (04-48).srt 5.5 kB
  • 11 - 2 - Finding a MAP Assignment (3-57).srt 5.2 kB
  • 4 - 7 - Working on and Submitting Programming Exercises (03-33).srt 4.6 kB

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