| 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 |