Coursera Stanford University - Probabilistic Graphical Models
Seeders : 0 Leechers : 1
| Torrent Hash : | 5648D60C0AFCFD91C0987EC1891949D63F645DC6 |
| Torrent Added : | 1 Year+ in Other - E-books |
| Torrent Size : | 1.36 GB |
Coursera Stanford University - Probabilistic Graphical Models
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Torrent File Content (186 files)
pgm
19 - 1 - Maximum Likelihood for Log-Linear Models (28-47).mp4 -
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
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