| pgm/19 - 1 - Maximum Likelihood for Log-Linear Models (28-47).mp4 |
34.6 MB |
| pgm/23 - 1 - Class Summary (24-38).mp4 |
32.2 MB |
| pgm/15 - 1 - Maximum Expected Utility (25-57).mp4 |
29 MB |
| pgm/20 - 6 - Learning General Graphs- Heuristic Search (23-36).mp4 |
26.8 MB |
| pgm/21 - 5 - Latent Variables (22-00).mp4 |
26.7 MB |
| pgm/3 - 2 - Temporal Models - DBNs (23-02).mp4 |
26.1 MB |
| pgm/6 - 6 - Log-Linear Models (22-08).mp4 |
25.8 MB |
| pgm/22 - 1 - Summary- Learning (20-11).mp4 |
25.7 MB |
| pgm/6 - 3 - Conditional Random Fields (22-22).mp4 |
25.1 MB |
| pgm/21 - 1 - Learning With Incomplete Data - Overview (21-34).mp4 |
24.9 MB |
| pgm/7 - 1 - Knowledge Engineering (23-05).mp4 |
24.7 MB |
| pgm/1 - 2 - Overview and Motivation (19-17).mp4 |
23 MB |
| pgm/20 - 4 - Bayesian Scores (20-35).mp4 |
22.6 MB |
| pgm/3 - 4 - Plate Models (20-08).mp4 |
22.5 MB |
| pgm/6 - 5 - I-maps and perfect maps (20-59).mp4 |
22.4 MB |
| pgm/2 - 5 - Independencies in Bayesian Networks (18-18).mp4 |
21.5 MB |
| pgm/18 - 5 - Bayesian Estimation for Bayesian Networks (17-02).mp4 |
21.2 MB |
| pgm/4 - 2 - Moving Data Around (16-07).mp4 |
20.8 MB |
| pgm/15 - 2 - Utility Functions (18-15).mp4 |
19.7 MB |
| pgm/2 - 1 - Semantics & Factorization (17-20).mp4 |
19.6 MB |
| pgm/15 - 3 - Value of Perfect Information (17-14).mp4 |
19.3 MB |
| pgm/6 - 2 - General Gibbs Distribution (15-52).mp4 |
18.9 MB |
| pgm/20 - 2 - Likelihood Scores (16-49).mp4 |
18.7 MB |
| pgm/18 - 3 - Bayesian Estimation (15-27).mp4 |
18.7 MB |
| pgm/21 - 2 - Expectation Maximization - Intro (16-17).mp4 |
18.1 MB |
| pgm/18 - 2 - Maximum Likelihood Estimation for Bayesian Networks (15-49).mp4 |
17.7 MB |
| pgm/4 - 1 - Basic Operations (13-59).mp4 |
17.7 MB |
| pgm/20 - 7 - Learning General Graphs- Search and Decomposability (15-46).mp4 |
17.6 MB |
| pgm/17 - 1 - Learning- Overview (15-35).mp4 |
17.5 MB |
| pgm/13 - 5 - Metropolis Hastings Algorithm (27-06).mp4 |
16.9 MB |
| pgm/4 - 5 - Control Statements- for, while, if statements (12-55).mp4 |
16.5 MB |
| pgm/18 - 4 - Bayesian Prediction (13-40).mp4 |
16.2 MB |
| pgm/4 - 6 - Vectorization (13-48).mp4 |
16.1 MB |
| pgm/5 - 2 - Tree-Structured CPDs (14-37).mp4 |
16 MB |
| pgm/5 - 3 - Independence of Causal Influence (13-08).mp4 |
15.9 MB |
| pgm/2 - 4 - Conditional Independence (12-38).mp4 |
15.5 MB |
| pgm/2 - 3 - Flow of Probabilistic Influence (14-36).mp4 |
15.5 MB |
| pgm/5 - 4 - Continuous Variables (13-25).mp4 |
15.3 MB |
| pgm/4 - 3 - Computing On Data (13-15).mp4 |
15.3 MB |
| pgm/18 - 1 - Maximum Likelihood Estimation (14-59).mp4 |
15.2 MB |
| pgm/19 - 2 - Maximum Likelihood for Conditional Random Fields (13-24).mp4 |
15.1 MB |
| pgm/20 - 5 - Learning Tree Structured Networks (12-05).mp4 |
14.5 MB |
| pgm/16 - 4 - Model Selection and Train Validation Test Sets (12-03).mp4 |
14.1 MB |
| pgm/13 - 1 - Simple Sampling (23-37).mp4 |
13.8 MB |
| pgm/3 - 3 - Temporal Models - HMMs (12-01).mp4 |
13.6 MB |
| pgm/14 - 1 - Inference in Temporal Models (19-43).mp4 |
13.6 MB |
| pgm/4 - 4 - Plotting Data (09-38).mp4 |
13.3 MB |
| pgm/9 - 1 - Belief Propagation (21-21).mp4 |
13.3 MB |
| pgm/10 - 7 - Loopy BP and Message Decoding (21-42).mp4 |
13.2 MB |
| pgm/21 - 3 - Analysis of EM Algorithm (11-32).mp4 |
12.9 MB |
| pgm/2 - 8 - Knowledge Engineering Example - SAMIAM (14-14).mp4 |
12.8 MB |
| pgm/21 - 4 - EM in Practice (11-17).mp4 |
12.7 MB |
| pgm/11 - 1 - Max Sum Message Passing (20-27).mp4 |
12.6 MB |
| pgm/16 - 6 - Regularization and Bias Variance (11-20).mp4 |
12.6 MB |
| pgm/6 - 1 - Pairwise Markov Networks (10-59).mp4 |
12.6 MB |
| pgm/20 - 3 - BIC and Asymptotic Consistency (11-26).mp4 |
12.5 MB |
| pgm/13 - 4 - Gibbs Sampling (19-26).mp4 |
12.5 MB |
| pgm/16 - 2 - Regularization- Cost Function (10-10).mp4 |
11.6 MB |
| pgm/3 - 1 - Overview of Template Models (10-55).mp4 |
11.6 MB |
| pgm/2 - 7 - Application - Medical Diagnosis (09-19).mp4 |
11.5 MB |
| pgm/19 - 3 - MAP Estimation for MRFs and CRFs (9-59).mp4 |
11.3 MB |
| pgm/12 - 2 - Dual Decomposition - Intuition (17-46).mp4 |
11.2 MB |
| pgm/16 - 1 - Regularization- The Problem of Overfitting (09-42).mp4 |
11.2 MB |
| pgm/8 - 3 - Variable Elimination Algorithm (16-17).mp4 |
11.1 MB |
| pgm/2 - 2 - Reasoning Patterns (09-59).mp4 |
10.8 MB |
| pgm/2 - 6 - Naive Bayes (09-52).mp4 |
10.6 MB |
| pgm/10 - 5 - Clique Trees and VE (16-17).mp4 |
10.6 MB |
| pgm/10 - 2 - Clique Tree Algorithm - Correctness (18-23).mp4 |
10.5 MB |
| pgm/6 - 7 - Shared Features in Log-Linear Models (08-28).mp4 |
10 MB |
| pgm/12 - 3 - Dual Decomposition - Algorithm (16-16).mp4 |
9.7 MB |
| pgm/9 - 2 - Properties of Cluster Graphs (15-00).mp4 |
9.7 MB |
| pgm/12 - 1 - Tractable MAP Problems (15-04).mp4 |
9.7 MB |
| pgm/5 - 1 - Overview- Structured CPDs (08-00).mp4 |
9.6 MB |
| pgm/8 - 5 - Graph-Based Perspective on Variable Elimination (15-25).mp4 |
9.5 MB |
| pgm/13 - 3 - Using a Markov Chain (15-27).mp4 |
9.5 MB |
| pgm/10 - 4 - Clique Trees and Independence (15-21).mp4 |
9.5 MB |
| pgm/13 - 2 - Markov Chain Monte Carlo (14-18).mp4 |
9.2 MB |
| pgm/10 - 6 - BP In Practice (15-38).mp4 |
9.2 MB |
| pgm/8 - 1 - Overview- Conditional Probability Queries (15-22).mp4 |
9 MB |
| pgm/16 - 5 - Diagnosing Bias vs Variance (07-42).mp4 |
9 MB |
| pgm/8 - 6 - Finding Elimination Orderings (11-58).mp4 |
8.8 MB |
| pgm/10 - 3 - Clique Tree Algorithm - Computation (16-18).mp4 |
8.7 MB |
| pgm/8 - 4 - Complexity of Variable Elimination (12-48).mp4 |
8.6 MB |
| pgm/16 - 3 - Evaluating a Hypothesis (07-35).mp4 |
8.5 MB |
| pgm/14 - 2 - Inference- Summary (12-45).mp4 |
7.8 MB |
| pgm/1 - 4 - Factors (06-40).mp4 |
7.4 MB |
| pgm/1 - 1 - Welcome! (05-35).mp4 |
7.1 MB |
| pgm/20 - 1 - Structure Learning Overview (5-49).mp4 |
6.7 MB |
| pgm/8 - 2 - Overview- MAP Inference (09-42).mp4 |
5.9 MB |
| pgm/6 - 4 - Independencies in Markov Networks (04-48).mp4 |
5.8 MB |
| pgm/1 - 3 - Distributions (04-56).mp4 |
5.8 MB |
| pgm/10 - 1 - Properties of Belief Propagation (9-31).mp4 |
5.8 MB |
| pgm/4 - 7 - Working on and Submitting Programming Exercises (03-33).mp4 |
5.5 MB |
| pgm/11 - 2 - Finding a MAP Assignment (3-57).mp4 |
2.7 MB |
| pgm/13 - 5 - Metropolis Hastings Algorithm (27-06).srt |
32 KB |
| pgm/19 - 1 - Maximum Likelihood for Log-Linear Models (28-47).srt |
31 KB |
| pgm/20 - 6 - Learning General Graphs- Heuristic Search (23-36).srt |
30 KB |
| pgm/15 - 1 - Maximum Expected Utility (25-57).srt |
30 KB |
| pgm/7 - 1 - Knowledge Engineering (23-05).srt |
28 KB |
| pgm/10 - 7 - Loopy BP and Message Decoding (21-42).srt |
27 KB |
| pgm/6 - 6 - Log-Linear Models (22-08).srt |
27 KB |
| pgm/3 - 2 - Temporal Models - DBNs (23-02).srt |
26 KB |
| pgm/13 - 1 - Simple Sampling (23-37).srt |
26 KB |
| pgm/21 - 5 - Latent Variables (22-00).srt |
25 KB |
| pgm/14 - 1 - Inference in Temporal Models (19-43).srt |
25 KB |
| pgm/1 - 2 - Overview and Motivation (19-17).srt |
25 KB |
| pgm/21 - 1 - Learning With Incomplete Data - Overview (21-34).srt |
25 KB |
| pgm/9 - 1 - Belief Propagation (21-21).srt |
24 KB |
| pgm/20 - 4 - Bayesian Scores (20-35).srt |
24 KB |
| pgm/6 - 3 - Conditional Random Fields (22-22).srt |
23 KB |
| pgm/3 - 4 - Plate Models (20-08).srt |
23 KB |
| pgm/2 - 8 - Knowledge Engineering Example - SAMIAM (14-14).srt |
23 KB |
| pgm/2 - 5 - Independencies in Bayesian Networks (18-18).srt |
23 KB |
| pgm/6 - 5 - I-maps and perfect maps (20-59).srt |
23 KB |
| pgm/11 - 1 - Max Sum Message Passing (20-27).srt |
22 KB |
| pgm/15 - 3 - Value of Perfect Information (17-14).srt |
22 KB |
| pgm/2 - 1 - Semantics & Factorization (17-20).srt |
21 KB |
| pgm/15 - 2 - Utility Functions (18-15).srt |
21 KB |
| pgm/10 - 2 - Clique Tree Algorithm - Correctness (18-23).srt |
20 KB |
| pgm/21 - 2 - Expectation Maximization - Intro (16-17).srt |
20 KB |
| pgm/12 - 2 - Dual Decomposition - Intuition (17-46).srt |
20 KB |
| pgm/13 - 4 - Gibbs Sampling (19-26).srt |
20 KB |
| pgm/17 - 1 - Learning- Overview (15-35).srt |
19 KB |
| pgm/20 - 7 - Learning General Graphs- Search and Decomposability (15-46).srt |
19 KB |
| pgm/12 - 1 - Tractable MAP Problems (15-04).srt |
19 KB |
| pgm/18 - 5 - Bayesian Estimation for Bayesian Networks (17-02).srt |
19 KB |
| pgm/20 - 2 - Likelihood Scores (16-49).srt |
19 KB |
| pgm/4 - 2 - Moving Data Around (16-07).srt |
19 KB |
| pgm/12 - 3 - Dual Decomposition - Algorithm (16-16).srt |
19 KB |
| pgm/13 - 3 - Using a Markov Chain (15-27).srt |
18 KB |
| pgm/18 - 3 - Bayesian Estimation (15-27).srt |
18 KB |
| pgm/10 - 5 - Clique Trees and VE (16-17).srt |
18 KB |
| pgm/8 - 3 - Variable Elimination Algorithm (16-17).srt |
18 KB |
| pgm/8 - 1 - Overview- Conditional Probability Queries (15-22).srt |
17 KB |
| pgm/10 - 6 - BP In Practice (15-38).srt |
17 KB |
| pgm/13 - 2 - Markov Chain Monte Carlo (14-18).srt |
17 KB |
| pgm/10 - 4 - Clique Trees and Independence (15-21).srt |
17 KB |
| pgm/5 - 2 - Tree-Structured CPDs (14-37).srt |
17 KB |
| pgm/18 - 2 - Maximum Likelihood Estimation for Bayesian Networks (15-49).srt |
17 KB |
| pgm/4 - 6 - Vectorization (13-48).srt |
17 KB |
| pgm/9 - 2 - Properties of Cluster Graphs (15-00).srt |
17 KB |
| pgm/4 - 1 - Basic Operations (13-59).srt |
16 KB |
| pgm/14 - 2 - Inference- Summary (12-45).srt |
16 KB |
| pgm/6 - 2 - General Gibbs Distribution (15-52).srt |
16 KB |
| pgm/10 - 3 - Clique Tree Algorithm - Computation (16-18).srt |
16 KB |
| pgm/16 - 4 - Model Selection and Train Validation Test Sets (12-03).srt |
16 KB |
| pgm/4 - 3 - Computing On Data (13-15).srt |
16 KB |
| pgm/19 - 2 - Maximum Likelihood for Conditional Random Fields (13-24).srt |
16 KB |
| pgm/2 - 3 - Flow of Probabilistic Influence (14-36).srt |
15 KB |
| pgm/18 - 1 - Maximum Likelihood Estimation (14-59).srt |
15 KB |
| pgm/21 - 4 - EM in Practice (11-17).srt |
15 KB |
| pgm/3 - 3 - Temporal Models - HMMs (12-01).srt |
15 KB |
| pgm/4 - 5 - Control Statements- for, while, if statements (12-55).srt |
15 KB |
| pgm/18 - 4 - Bayesian Prediction (13-40).srt |
15 KB |
| pgm/2 - 4 - Conditional Independence (12-38).srt |
15 KB |
| pgm/16 - 6 - Regularization and Bias Variance (11-20).srt |
15 KB |
| pgm/8 - 5 - Graph-Based Perspective on Variable Elimination (15-25).srt |
15 KB |
| pgm/5 - 4 - Continuous Variables (13-25).srt |
15 KB |
| pgm/8 - 6 - Finding Elimination Orderings (11-58).srt |
14 KB |
| pgm/20 - 5 - Learning Tree Structured Networks (12-05).srt |
14 KB |
| pgm/5 - 3 - Independence of Causal Influence (13-08).srt |
14 KB |
| pgm/20 - 3 - BIC and Asymptotic Consistency (11-26).srt |
14 KB |
| pgm/6 - 1 - Pairwise Markov Networks (10-59).srt |
13 KB |
| pgm/16 - 2 - Regularization- Cost Function (10-10).srt |
13 KB |
| pgm/16 - 1 - Regularization- The Problem of Overfitting (09-42).srt |
13 KB |
| pgm/21 - 3 - Analysis of EM Algorithm (11-32).srt |
13 KB |
| pgm/8 - 4 - Complexity of Variable Elimination (12-48).srt |
13 KB |
| pgm/3 - 1 - Overview of Template Models (10-55).srt |
13 KB |
| pgm/19 - 3 - MAP Estimation for MRFs and CRFs (9-59).srt |
12 KB |
| pgm/2 - 7 - Application - Medical Diagnosis (09-19).srt |
12 KB |
| pgm/2 - 2 - Reasoning Patterns (09-59).srt |
12 KB |
| pgm/4 - 4 - Plotting Data (09-38).srt |
11 KB |
| pgm/8 - 2 - Overview- MAP Inference (09-42).srt |
11 KB |
| pgm/2 - 6 - Naive Bayes (09-52).srt |
11 KB |
| pgm/10 - 1 - Properties of Belief Propagation (9-31).srt |
10 KB |
| pgm/16 - 5 - Diagnosing Bias vs Variance (07-42).srt |
10 KB |
| pgm/1 - 1 - Welcome! (05-35).srt |
10 KB |
| pgm/5 - 1 - Overview- Structured CPDs (08-00).srt |
10 KB |
| pgm/16 - 3 - Evaluating a Hypothesis (07-35).srt |
9 KB |
| pgm/6 - 7 - Shared Features in Log-Linear Models (08-28).srt |
9 KB |
| pgm/1 - 4 - Factors (06-40).srt |
8 KB |
| pgm/20 - 1 - Structure Learning Overview (5-49).srt |
8 KB |
| pgm/1 - 3 - Distributions (04-56).srt |
7 KB |
| pgm/6 - 4 - Independencies in Markov Networks (04-48).srt |
5 KB |
| pgm/11 - 2 - Finding a MAP Assignment (3-57).srt |
5 KB |
| pgm/4 - 7 - Working on and Submitting Programming Exercises (03-33).srt |
5 KB |
Magnet link
