视频选集
（1/94）
自动连播
001_Welcome! (05 -35)
05:36
002_Overview and Motivation (19 -17)
19:18
003_Distributions (04 -56)
04:58
004_Factors (06 -40)
06:41
005_Semantics & Factorization (17 -20)
17:21
006_Reasoning Patterns (09 -59)
10:00
007_Flow of Probabilistic Influence (14 -36)
14:37
008_Conditional Independence (12 -38)
12:39
009_Independencies in Bayesian Networks (18 -18)
18:19
010_Naive Bayes (09 -52)
09:53
011_Application - Medical Diagnosis (09 -19)
09:20
012_Knowledge Engineering Example - SAMIAM (14 -14)
14:15
013_Overview of Template Models (10 -55)
10:56
014_Temporal Models - DBNs (23 -02)
23:03
015_Temporal Models - HMMs (12 -01)
12:02
016_Plate Models (20 -08)
20:09
017_Basic Operations (13 -59)
14:00
018_Moving Data Around (16 -07)
16:08
019_Computing On Data (13 -15)
13:16
020_Plotting Data (09 -38)
09:39
021_Control Statements - for, while, if statements (12 -55)
12:57
022_Vectorization (13 -48)
13:49
023_Working on and Submitting Programming Exercises (03 -33)
03:34
024_Overview - Structured CPDs (08 -00)
08:01
025_Tree-Structured CPDs (14 -37)
14:38
026_Independence of Causal Influence (13 -08)
13:09
027_Continuous Variables (13 -25)
13:27
028_Pairwise Markov Networks (10 -59)
11:00
029_General Gibbs Distribution (15 -52)
15:53
030_Conditional Random Fields (22 -22)
22:23
031_Independencies in Markov Networks (04 -48)
04:49
032_I-maps and perfect maps (20 -59)
21:00
033_Log-Linear Models (22 -08)
22:10
034_Shared Features in Log-Linear Models (08 -28)
08:29
035_Knowledge Engineering (23 -05)
23:06
036_Overview - Conditional Probability Queries (15 -22)
15:23
037_Overview - MAP Inference (09 -42)
09:48
038_Variable Elimination Algorithm (16 -17)
16:18
039_Complexity of Variable Elimination (12 -48)
12:49
040_Graph-Based Perspective on Variable Elimination (15 -25)
15:26
041_Finding Elimination Orderings (11 -58)
11:59
042_Belief Propagation (21 -21)
21:22
043_Properties of Cluster Graphs (15 -00)
15:01
044_Properties of Belief Propagation (9 -31)
09:32
045_Clique Tree Algorithm - Correctness (18 -23)
18:24
046_Clique Tree Algorithm - Computation (16 -18)
16:19
047_Clique Trees and Independence (15 -21)
15:22
048_Clique Trees and VE (16 -17)
16:19
049_BP In Practice (15 -38)
15:39
050_Loopy BP and Message Decoding (21 -42)
21:43
051_Max Sum Message Passing (20 -27)
20:28
052_Finding a MAP Assignment (3 -57)
03:58
053_Tractable MAP Problems (15 -04)
15:05
054_Dual Decomposition - Intuition (17 -46)
17:47
055_Dual Decomposition - Algorithm (16 -16)
16:17
056_Simple Sampling (23 -37)
23:38
057_Markov Chain Monte Carlo (14 -18)
14:19
058_Using a Markov Chain (15 -27)
15:28
059_Gibbs Sampling (19 -26)
19:27
060_Metropolis Hastings Algorithm (27 -06)
27:06
061_Inference in Temporal Models (19 -43)
19:44
062_Inference - Summary (12 -45)
12:47
063_Maximum Expected Utility (25 -57)
25:59
064_Utility Functions (18 -15)
18:16
065_Value of Perfect Information (17 -14)
17:15
066_Regularization - The Problem of Overfitting (09 -42)
09:43
067_Regularization - Cost Function (10 -10)
10:11
068_Evaluating a Hypothesis (07 -35)
07:36
069_Model Selection and Train Validation Test Sets (12 -03)
12:04
070_Diagnosing Bias vs Variance (07 -42)
07:43
071_Regularization and Bias Variance (11 -20)
11:21
072_Learning - Overview (15 -35)
15:36
073_Maximum Likelihood Estimation (14 -59)
15:00
074_Maximum Likelihood Estimation for Bayesian Networks (15 -49)
15:50
075_Bayesian Estimation (15 -27)
15:28
076_Bayesian Prediction (13 -40)
13:41
077_Bayesian Estimation for Bayesian Networks (17 -02)
17:03
078_Maximum Likelihood for Log-Linear Models (28 -47)
28:48
079_Maximum Likelihood for Conditional Random Fields (13 -24)
13:25
080_MAP Estimation for MRFs and CRFs (9 -59)
10:00
081_Structure Learning Overview (5 -49)
05:51
082_Likelihood Scores (16 -49)
16:50
083_BIC and Asymptotic Consistency (11 -26)
11:28
084_Bayesian Scores (20 -35)
20:36
085_Learning Tree Structured Networks (12 -05)
12:06
086_Learning General Graphs - Heuristic Search (23 -36)
23:37
087_Learning General Graphs - Search and Decomposability (15 -46)
15:47
088_Learning With Incomplete Data - Overview (21 -34)
21:35
089_Expectation Maximization - Intro (16 -17)
16:18
090_Analysis of EM Algorithm (11 -32)
11:35
091_EM in Practice (11 -17)
11:18
092_Latent Variables (22 -00)
22:01
093_Summary - Learning (20 -11)
20:12
094_Class Summary (24 -38)
24:39
【B站AI论坛】欢迎写代码、做产品的AI开发者发帖加入
共建AI开发者交流地>>
顶部