Pgm | 5648D60C0AFCFD91C0987EC1891949D63F645DC6

Pgm

Name	pgm	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	1.5 GB
Total Files	186
Last Seen
Hash	5648D60C0AFCFD91C0987EC1891949D63F645DC6

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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
Total files 186