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pgm
magnet:?xt=urn:btih:5648D60C0AFCFD91C0987EC1891949D63F645DC6&dn=pgm
磁力链接详情
Hash值:
5648D60C0AFCFD91C0987EC1891949D63F645DC6
点击数:
78
文件大小:
1.36 GB
文件数量:
94
创建日期:
2012-5-19 17:21
最后访问:
2024-7-13 14:14
访问标签:
pgm
文件列表详情
19 - 1 - Maximum Likelihood for Log-Linear Models (28-47).mp4 34.61 MB
23 - 1 - Class Summary (24-38).mp4 32.21 MB
15 - 1 - Maximum Expected Utility (25-57).mp4 28.99 MB
20 - 6 - Learning General Graphs- Heuristic Search (23-36).mp4 26.77 MB
21 - 5 - Latent Variables (22-00).mp4 26.7 MB
3 - 2 - Temporal Models - DBNs (23-02).mp4 26.07 MB
6 - 6 - Log-Linear Models (22-08).mp4 25.77 MB
22 - 1 - Summary- Learning (20-11).mp4 25.69 MB
6 - 3 - Conditional Random Fields (22-22).mp4 25.06 MB
21 - 1 - Learning With Incomplete Data - Overview (21-34).mp4 24.86 MB
7 - 1 - Knowledge Engineering (23-05).mp4 24.65 MB
1 - 2 - Overview and Motivation (19-17).mp4 23 MB
20 - 4 - Bayesian Scores (20-35).mp4 22.62 MB
3 - 4 - Plate Models (20-08).mp4 22.48 MB
6 - 5 - I-maps and perfect maps (20-59).mp4 22.41 MB
2 - 5 - Independencies in Bayesian Networks (18-18).mp4 21.54 MB
18 - 5 - Bayesian Estimation for Bayesian Networks (17-02).mp4 21.16 MB
4 - 2 - Moving Data Around (16-07).mp4 20.77 MB
15 - 2 - Utility Functions (18-15).mp4 19.68 MB
2 - 1 - Semantics & Factorization (17-20).mp4 19.56 MB
15 - 3 - Value of Perfect Information (17-14).mp4 19.28 MB
6 - 2 - General Gibbs Distribution (15-52).mp4 18.93 MB
20 - 2 - Likelihood Scores (16-49).mp4 18.73 MB
18 - 3 - Bayesian Estimation (15-27).mp4 18.66 MB
21 - 2 - Expectation Maximization - Intro (16-17).mp4 18.07 MB
18 - 2 - Maximum Likelihood Estimation for Bayesian Networks (15-49).mp4 17.72 MB
4 - 1 - Basic Operations (13-59).mp4 17.71 MB
20 - 7 - Learning General Graphs- Search and Decomposability (15-46).mp4 17.64 MB
17 - 1 - Learning- Overview (15-35).mp4 17.51 MB
13 - 5 - Metropolis Hastings Algorithm (27-06).mp4 16.91 MB
4 - 5 - Control Statements- for, while, if statements (12-55).mp4 16.49 MB
18 - 4 - Bayesian Prediction (13-40).mp4 16.21 MB
4 - 6 - Vectorization (13-48).mp4 16.09 MB
5 - 2 - Tree-Structured CPDs (14-37).mp4 16.04 MB
5 - 3 - Independence of Causal Influence (13-08).mp4 15.87 MB
2 - 4 - Conditional Independence (12-38).mp4 15.52 MB
2 - 3 - Flow of Probabilistic Influence (14-36).mp4 15.47 MB
5 - 4 - Continuous Variables (13-25).mp4 15.34 MB
4 - 3 - Computing On Data (13-15).mp4 15.25 MB
18 - 1 - Maximum Likelihood Estimation (14-59).mp4 15.15 MB
19 - 2 - Maximum Likelihood for Conditional Random Fields (13-24).mp4 15.1 MB
20 - 5 - Learning Tree Structured Networks (12-05).mp4 14.46 MB
16 - 4 - Model Selection and Train Validation Test Sets (12-03).mp4 14.07 MB
13 - 1 - Simple Sampling (23-37).mp4 13.78 MB
3 - 3 - Temporal Models - HMMs (12-01).mp4 13.58 MB
14 - 1 - Inference in Temporal Models (19-43).mp4 13.56 MB
4 - 4 - Plotting Data (09-38).mp4 13.32 MB
9 - 1 - Belief Propagation (21-21).mp4 13.25 MB
10 - 7 - Loopy BP and Message Decoding (21-42).mp4 13.15 MB
21 - 3 - Analysis of EM Algorithm (11-32).mp4 12.88 MB
2 - 8 - Knowledge Engineering Example - SAMIAM (14-14).mp4 12.76 MB
21 - 4 - EM in Practice (11-17).mp4 12.69 MB
11 - 1 - Max Sum Message Passing (20-27).mp4 12.65 MB
16 - 6 - Regularization and Bias Variance (11-20).mp4 12.6 MB
6 - 1 - Pairwise Markov Networks (10-59).mp4 12.56 MB
20 - 3 - BIC and Asymptotic Consistency (11-26).mp4 12.53 MB
13 - 4 - Gibbs Sampling (19-26).mp4 12.5 MB
16 - 2 - Regularization- Cost Function (10-10).mp4 11.63 MB
3 - 1 - Overview of Template Models (10-55).mp4 11.57 MB
2 - 7 - Application - Medical Diagnosis (09-19).mp4 11.51 MB
19 - 3 - MAP Estimation for MRFs and CRFs (9-59).mp4 11.29 MB
12 - 2 - Dual Decomposition - Intuition (17-46).mp4 11.2 MB
16 - 1 - Regularization- The Problem of Overfitting (09-42).mp4 11.15 MB
8 - 3 - Variable Elimination Algorithm (16-17).mp4 11.11 MB
2 - 2 - Reasoning Patterns (09-59).mp4 10.78 MB
2 - 6 - Naive Bayes (09-52).mp4 10.63 MB
10 - 5 - Clique Trees and VE (16-17).mp4 10.55 MB
10 - 2 - Clique Tree Algorithm - Correctness (18-23).mp4 10.48 MB
6 - 7 - Shared Features in Log-Linear Models (08-28).mp4 10.02 MB
12 - 3 - Dual Decomposition - Algorithm (16-16).mp4 9.74 MB
9 - 2 - Properties of Cluster Graphs (15-00).mp4 9.73 MB
12 - 1 - Tractable MAP Problems (15-04).mp4 9.69 MB
5 - 1 - Overview- Structured CPDs (08-00).mp4 9.65 MB
8 - 5 - Graph-Based Perspective on Variable Elimination (15-25).mp4 9.55 MB
13 - 3 - Using a Markov Chain (15-27).mp4 9.53 MB
10 - 4 - Clique Trees and Independence (15-21).mp4 9.52 MB
13 - 2 - Markov Chain Monte Carlo (14-18).mp4 9.21 MB
10 - 6 - BP In Practice (15-38).mp4 9.2 MB
8 - 1 - Overview- Conditional Probability Queries (15-22).mp4 9.01 MB
16 - 5 - Diagnosing Bias vs Variance (07-42).mp4 8.97 MB
8 - 6 - Finding Elimination Orderings (11-58).mp4 8.77 MB
10 - 3 - Clique Tree Algorithm - Computation (16-18).mp4 8.72 MB
8 - 4 - Complexity of Variable Elimination (12-48).mp4 8.58 MB
16 - 3 - Evaluating a Hypothesis (07-35).mp4 8.48 MB
14 - 2 - Inference- Summary (12-45).mp4 7.83 MB
1 - 4 - Factors (06-40).mp4 7.37 MB
1 - 1 - Welcome! (05-35).mp4 7.11 MB
20 - 1 - Structure Learning Overview (5-49).mp4 6.66 MB
8 - 2 - Overview- MAP Inference (09-42).mp4 5.87 MB
6 - 4 - Independencies in Markov Networks (04-48).mp4 5.84 MB
1 - 3 - Distributions (04-56).mp4 5.81 MB
10 - 1 - Properties of Belief Propagation (9-31).mp4 5.75 MB
4 - 7 - Working on and Submitting Programming Exercises (03-33).mp4 5.5 MB
11 - 2 - Finding a MAP Assignment (3-57).mp4 2.67 MB
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