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UCLA贝叶斯网络课程 Bayesian Networks

1.3万

12

2019-02-19 08:14:17

https://www.youtube.com/watch?v=-6A6b_Z4bIg&list=PLlDG_zCuBub6ywAIrM1DfJp8xaeVjyvwx

贝叶斯网络

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01.1a. Course Overview with a Historical Perspective on AI

58:32

02.1b. Propositional Logic (Chapter 2)

33:34

03.2a. Probability Calculus- Beliefs and Hard Evidence (Chapter 3)

42:22

04.2b. Probability Calculus- Soft Evidence (Chapter 3)

19:39

05.3a. Bayesian Networks- Syntax and Semantics (Chapter 4)

38:53

06.3b. Bayesian Networks- Independence and d-Separation (Chapter 4)

35:17

07.4a. Probabilistic Queries and their Complexity (Chapter 5)

42:12

08.4b. Building Bayesian Networks I (Chapter 5)

36:17

09.5a. Building Bayesian Networks II (Chapter 5)

45:56

10.5b. Building Bayesian Networks III (Chapter 5)

29:51

11.6a. Inference by Variable Elimination I (Chapter 6)

35:31

12.6b. Inference by Variable Elimination II (Chapter 6)

38:32

13.7a. The Jointree Algorithm (Chapter 7)

47:54

14.7b. Inference by Conditioning (Chapter 8)

32:48

15.8a. Arithmetic Circuits I (Chapter 12)

43:57

16.8b. Arithmetic Circuits II (Chapter 12)

26:45

17.9a. Arithmetic Circuits & SPNs

43:14

18.9b. Arithmetic Circuits & PSDDs

33:55

19.10a. Loopy Belief Propagation (Chapter 14)

25:27

20.10b. Relax, Compensate, then Recover (Chapter 14)

22:24

21.11a. Learning Parameters- Complete Data (Chapter 17)

32:28

22.11b. Learning Parameters- Incomplete Data (Chapter 17)

29:18

23.12a. Learning Network Structure I (Chapter 17)

31:22

24.12b. Learning Network Structure II (Chapter 17)

30:46

25.13a. Bayesian Learning- Discrete Parameter Sets I (Chapter 18)

32:59

26.13b. Bayesian Learning- Discrete Parameter Sets II (Chapter 18)

20:28

27.14. Bayesian Learning- Dirichlet Priors (Chapter 18)

46:03

28.15a. Causality I

39:26

29.15b. Causality II

36:33

30.16. Sensitivity Analysis (Chapter 16)

54:55

31.17a. Reasoning about Classifiers

49:37

32.17b. Explaining Classifiers

25:59

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