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87. Mega-Recitation 7_ Near Misses, Arch Learning - 3
2年前 873观看
人工智能
大学课程 / 计算机
itunes 人工智能是计算机科学的一个分支,它企图了解智能的实质,并生产出一种新的能以人类智能相似的方式做出反应的智能机器,该领域的研究包括机器人、语言识别、图像识别、自然语言处理和专家系统等。
共87集
17.1万人观看
1
Lecture 1_ Introduction and Scope - 1
15:48
2
Lecture 1_ Introduction and Scope - 2
15:56
3
Lecture 1_ Introduction and Scope - 3
15:45
4
Lecture 2_ Reasoning_ Goal Trees and Problem Solving - 1
15:21
5
Lecture 2_ Reasoning_ Goal Trees and Problem Solving - 2
15:30
6
Lecture 2_ Reasoning_ Goal Trees and Problem Solving - 3
15:21
7
Lecture 3_ Reasoning_ Goal Trees and Rule-Based Expert Systems - 1
16:40
8
Lecture 3_ Reasoning_ Goal Trees and Rule-Based Expert Systems - 2
16:48
9
Lecture 3_ Reasoning_ Goal Trees and Rule-Based Expert Systems - 3
16:34
10
Lecture 4_ Search_ Depth-First, Hill Climbing, Beam - 1
16:15
11
Lecture 4_ Search_ Depth-First, Hill Climbing, Beam - 2
16:22
12
Lecture 4_ Search_ Depth-First, Hill Climbing, Beam - 3
16:09
13
Lecture 5_ Search_ Optimal, Branch and Bound, A_ - 1
16:14
14
Lecture 5_ Search_ Optimal, Branch and Bound, A_ - 2
16:15
15
Lecture 5_ Search_ Optimal, Branch and Bound, A_ - 3
16:12
16
Lecture 6_ Search_ Games, Minimax, and Alpha-Beta - 1
16:07
17
Lecture 6_ Search_ Games, Minimax, and Alpha-Beta - 2
16:11
18
Lecture 6_ Search_ Games, Minimax, and Alpha-Beta - 3
16:00
19
Lecture 7_ Constraints_ Interpreting Line Drawings - 1
16:26
20
Lecture 7_ Constraints_ Interpreting Line Drawings - 2
16:34
21
Lecture 7_ Constraints_ Interpreting Line Drawings - 3
16:24
22
Lecture 8_ Constraints_ Search, Domain Reduction - 1
15:10
23
Lecture 8_ Constraints_ Search, Domain Reduction - 2
15:17
24
Lecture 8_ Constraints_ Search, Domain Reduction - 3
15:02
25
Lecture 9_ Constraints_ Visual Object Recognition - 1
17:12
26
Lecture 9_ Constraints_ Visual Object Recognition - 2
17:20
27
Lecture 9_ Constraints_ Visual Object Recognition - 3
17:08
28
Lecture 10_ Introduction to Learning, Nearest Neighbors - 1
16:40
29
Lecture 10_ Introduction to Learning, Nearest Neighbors - 2
16:44
30
Lecture 10_ Introduction to Learning, Nearest Neighbors - 3
16:39
31
Lecture 11_ Learning_ Identification Trees, Disorder - 1
16:34
32
Lecture 11_ Learning_ Identification Trees, Disorder - 2
16:43
33
Lecture 11_ Learning_ Identification Trees, Disorder - 3
16:30
34
Lecture 12_ Learning_ Neural Nets, Back Propagation - 1
16:00
35
Lecture 12_ Learning_ Neural Nets, Back Propagation - 2
16:05
36
Lecture 12_ Learning_ Neural Nets, Back Propagation - 3
15:56
37
Lecture 13_ Learning_ Genetic Algorithms - 1
15:47
38
Lecture 13_ Learning_ Genetic Algorithms - 2
15:51
39
Lecture 13_ Learning_ Genetic Algorithms - 3
15:45
40
Lecture 14_ Learning_ Sparse Spaces, Phonology - 1
15:58
41
Lecture 14_ Learning_ Sparse Spaces, Phonology - 2
16:05
42
Lecture 14_ Learning_ Sparse Spaces, Phonology - 3
15:53
43
Lecture 15_ Learning_ Near Misses, Felicity Conditions - 1
15:39
44
Lecture 15_ Learning_ Near Misses, Felicity Conditions - 2
15:47
45
Lecture 15_ Learning_ Near Misses, Felicity Conditions - 3
15:40
46
Lecture 16_ Learning_ Support Vector Machines - 1
16:33
47
Lecture 16_ Learning_ Support Vector Machines - 2
16:40
48
Lecture 16_ Learning_ Support Vector Machines - 3
16:33
49
Lecture 17_ Learning_ Boosting - 1
17:15
50
Lecture 17_ Learning_ Boosting - 2
17:21
51
Lecture 17_ Learning_ Boosting - 3
17:13
52
Lecture 18_ Representations_ Classes, Trajectories, Transitions - 1
16:22
53
Lecture 18_ Representations_ Classes, Trajectories, Transitions - 2
16:26
54
Lecture 18_ Representations_ Classes, Trajectories, Transitions - 3
16:17
55
Lecture 19_ Architectures_ GPS, SOAR, Subsumption, Society of Mind - 1
16:24
56
Lecture 19_ Architectures_ GPS, SOAR, Subsumption, Society of Mind - 2
16:28
57
Lecture 19_ Architectures_ GPS, SOAR, Subsumption, Society of Mind - 3
16:22
58
Lecture 21_ Probabilistic Inference I - 1
16:11
59
Lecture 21_ Probabilistic Inference I - 2
16:12
60
Lecture 21_ Probabilistic Inference I - 3
16:06
61
Lecture 22_ Probabilistic Inference II - 1
16:17
62
Lecture 22_ Probabilistic Inference II - 2
16:24
63
Lecture 22_ Probabilistic Inference II - 3
16:13
64
Lecture 23_ Model Merging, Cross-Modal Coupling, Course Summary - 1
16:32
65
Lecture 23_ Model Merging, Cross-Modal Coupling, Course Summary - 2
16:38
66
Lecture 23_ Model Merging, Cross-Modal Coupling, Course Summary - 3
16:24
67
Mega-Recitation 1_ Rule-Based Systems - 1
15:42
68
Mega-Recitation 1_ Rule-Based Systems - 2
15:46
69
Mega-Recitation 1_ Rule-Based Systems - 3
15:42
70
Mega-Recitation 2_ Basic Search, Optimal Search - 1
17:21
71
Mega-Recitation 2_ Basic Search, Optimal Search - 2
17:23
72
Mega-Recitation 2_ Basic Search, Optimal Search - 3
17:17
73
Mega-Recitation 3_ Games, Minimax, Alpha-Beta - 1
17:01
74
Mega-Recitation 3_ Games, Minimax, Alpha-Beta - 2
17:02
75
Mega-Recitation 3_ Games, Minimax, Alpha-Beta - 3
16:56
76
Mega-Recitation 4_ Neural Nets - 1
17:35
77
Mega-Recitation 4_ Neural Nets - 2
17:37
78
Mega-Recitation 4_ Neural Nets - 3
17:32
79
Mega-Recitation 5_ Support Vector Machines - 1
16:40
80
Mega-Recitation 5_ Support Vector Machines - 2
16:41
81
Mega-Recitation 5_ Support Vector Machines - 3
16:39
82
Mega-Recitation 6_ Boosting - 1
16:41
83
Mega-Recitation 6_ Boosting - 2
16:44
84
Mega-Recitation 6_ Boosting - 3
16:41
85
Mega-Recitation 7_ Near Misses, Arch Learning - 1
11:04
86
Mega-Recitation 7_ Near Misses, Arch Learning - 2
11:06
87
Mega-Recitation 7_ Near Misses, Arch Learning - 3
10:59
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