【94集全】斯坦福大学Coursera公开课《概率图模型》
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【94集全】斯坦福大学Coursera公开课《概率图模型》
视频选集
（1/94）
001_welcome! (05 -35)_1
05:36
002_overview and motivation (19 -17)_1
19:18
003_distributions (04 -56)_1
04:58
004_factors (06 -40)_1
06:41
005_semantics & factorization (17 -20)_1
17:21
006_reasoning patterns (09 -59)_1
10:01
007_flow of probabilistic influence (14 -36)_1
14:37
008_conditional independence (12 -38)_1
12:39
009_independencies in bayesian networks (18 -18)_1
18:19
010_naive bayes (09 -52)_1
09:53
011_application - medical diagnosis (09 -19)_1
09:20
012_knowledge engineering example - samiam (14 -14
14:15
013_overview of template models (10 -55)_1
10:56
014_temporal models - dbns (23 -02)_1
23:03
015_temporal models - hmms (12 -01)_1
12:02
016_plate models (20 -08)_1
20:10
017_basic operations (13 -59)_1
14:00
018_moving data around (16 -07)_1
16:08
019_computing on data (13 -15)_1
13:16
020_plotting data (09 -38)_1
09:39
021_control statements - for, while, if statements
12:57
022_vectorization (13 -48)_1
13:49
023_working on and submitting programming exercise
03:34
024_overview - structured cpds (08 -00)_1
08:01
025_tree-structured cpds (14 -37)_1
14:38
026_independence of causal influence (13 -08)_1
13:09
027_continuous variables (13 -25)_1
13:27
028_pairwise markov networks (10 -59)_1
11:00
029_general gibbs distribution (15 -52)_1
15:53
030_conditional random fields (22 -22)_1
22:23
031_independencies in markov networks (04 -48)_1
04:49
032_i-maps and perfect maps (20 -59)_1
21:00
033_log-linear models (22 -08)_1
22:10
034_shared features in log-linear models (08 -28)_
08:29
035_knowledge engineering (23 -05)_1
23:06
036_overview - conditional probability queries (15
15:23
037_overview - map inference (09 -42)_1
09:48
038_variable elimination algorithm (16 -17)_1
16:18
039_complexity of variable elimination (12 -48)_1
12:49
040_graph-based perspective on variable eliminatio
15:26
041_finding elimination orderings (11 -58)_1
11:59
042_belief propagation (21 -21)_1
21:22
043_properties of cluster graphs (15 -00)_1
15:02
044_properties of belief propagation (9 -31)_1
09:33
045_clique tree algorithm - correctness (18 -23)_1
18:25
046_clique tree algorithm - computation (16 -18)_1
16:19
047_clique trees and independence (15 -21)_1
15:22
048_clique trees and ve (16 -17)_1
16:19
049_bp in practice (15 -38)_1
15:39
050_loopy bp and message decoding (21 -42)_1
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
09:43
067_regularization - cost function (10 -10)
10:12
068_evaluating a hypothesis (07 -35)
07:36
069_model selection and train validation test sets
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 net
15:50
075_bayesian estimation (15 -27)
15:28
076_bayesian prediction (13 -40)
13:41
077_bayesian estimation for bayesian networks (17
17:03
078_maximum likelihood for log-linear models (28 -
28:48
079_maximum likelihood for conditional random fiel
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
23:37
087_learning general graphs - search and decomposa
15:47
088_learning with incomplete data - overview
21:36
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
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