74. Mega-Recitation 3_ Games, Minimax, Alpha-Beta - 2
2年前 732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
1Lecture 1_ Introduction and Scope - 115:48- 2Lecture 1_ Introduction and Scope - 215:56
- 3Lecture 1_ Introduction and Scope - 315:45
4Lecture 2_ Reasoning_ Goal Trees and Problem Solving - 115:21- 5Lecture 2_ Reasoning_ Goal Trees and Problem Solving - 215:30
- 6Lecture 2_ Reasoning_ Goal Trees and Problem Solving - 315:21
7Lecture 3_ Reasoning_ Goal Trees and Rule-Based Expert Systems - 116:40- 8Lecture 3_ Reasoning_ Goal Trees and Rule-Based Expert Systems - 216:48
- 9Lecture 3_ Reasoning_ Goal Trees and Rule-Based Expert Systems - 316:34
10Lecture 4_ Search_ Depth-First, Hill Climbing, Beam - 116:15- 11Lecture 4_ Search_ Depth-First, Hill Climbing, Beam - 216:22
- 12Lecture 4_ Search_ Depth-First, Hill Climbing, Beam - 316:09
13Lecture 5_ Search_ Optimal, Branch and Bound, A_ - 116:14- 14Lecture 5_ Search_ Optimal, Branch and Bound, A_ - 216:15
- 15Lecture 5_ Search_ Optimal, Branch and Bound, A_ - 316:12
16Lecture 6_ Search_ Games, Minimax, and Alpha-Beta - 116:07- 17Lecture 6_ Search_ Games, Minimax, and Alpha-Beta - 216:11
- 18Lecture 6_ Search_ Games, Minimax, and Alpha-Beta - 316:00
19Lecture 7_ Constraints_ Interpreting Line Drawings - 116:26- 20Lecture 7_ Constraints_ Interpreting Line Drawings - 216:34
- 21Lecture 7_ Constraints_ Interpreting Line Drawings - 316:24
22Lecture 8_ Constraints_ Search, Domain Reduction - 115:10- 23Lecture 8_ Constraints_ Search, Domain Reduction - 215:17
- 24Lecture 8_ Constraints_ Search, Domain Reduction - 315:02
25Lecture 9_ Constraints_ Visual Object Recognition - 117:12- 26Lecture 9_ Constraints_ Visual Object Recognition - 217:20
- 27Lecture 9_ Constraints_ Visual Object Recognition - 317:08
28Lecture 10_ Introduction to Learning, Nearest Neighbors - 116:40- 29Lecture 10_ Introduction to Learning, Nearest Neighbors - 216:44
- 30Lecture 10_ Introduction to Learning, Nearest Neighbors - 316:39
31Lecture 11_ Learning_ Identification Trees, Disorder - 116:34- 32Lecture 11_ Learning_ Identification Trees, Disorder - 216:43
- 33Lecture 11_ Learning_ Identification Trees, Disorder - 316:30
34Lecture 12_ Learning_ Neural Nets, Back Propagation - 116:00- 35Lecture 12_ Learning_ Neural Nets, Back Propagation - 216:05
- 36Lecture 12_ Learning_ Neural Nets, Back Propagation - 315:56
37Lecture 13_ Learning_ Genetic Algorithms - 115:47- 38Lecture 13_ Learning_ Genetic Algorithms - 215:51
- 39Lecture 13_ Learning_ Genetic Algorithms - 315:45
40Lecture 14_ Learning_ Sparse Spaces, Phonology - 115:58- 41Lecture 14_ Learning_ Sparse Spaces, Phonology - 216:05
- 42Lecture 14_ Learning_ Sparse Spaces, Phonology - 315:53
43Lecture 15_ Learning_ Near Misses, Felicity Conditions - 115:39- 44Lecture 15_ Learning_ Near Misses, Felicity Conditions - 215:47
- 45Lecture 15_ Learning_ Near Misses, Felicity Conditions - 315:40
46Lecture 16_ Learning_ Support Vector Machines - 116:33- 47Lecture 16_ Learning_ Support Vector Machines - 216:40
- 48Lecture 16_ Learning_ Support Vector Machines - 316:33
49Lecture 17_ Learning_ Boosting - 117:15- 50Lecture 17_ Learning_ Boosting - 217:21
- 51Lecture 17_ Learning_ Boosting - 317:13
52Lecture 18_ Representations_ Classes, Trajectories, Transitions - 116:22- 53Lecture 18_ Representations_ Classes, Trajectories, Transitions - 216:26
- 54Lecture 18_ Representations_ Classes, Trajectories, Transitions - 316:17
55Lecture 19_ Architectures_ GPS, SOAR, Subsumption, Society of Mind - 116:24- 56Lecture 19_ Architectures_ GPS, SOAR, Subsumption, Society of Mind - 216:28
- 57Lecture 19_ Architectures_ GPS, SOAR, Subsumption, Society of Mind - 316:22
58Lecture 21_ Probabilistic Inference I - 116:11- 59Lecture 21_ Probabilistic Inference I - 216:12
- 60Lecture 21_ Probabilistic Inference I - 316:06
61Lecture 22_ Probabilistic Inference II - 116:17- 62Lecture 22_ Probabilistic Inference II - 216:24
- 63Lecture 22_ Probabilistic Inference II - 316:13
64Lecture 23_ Model Merging, Cross-Modal Coupling, Course Summary - 116:32- 65Lecture 23_ Model Merging, Cross-Modal Coupling, Course Summary - 216:38
- 66Lecture 23_ Model Merging, Cross-Modal Coupling, Course Summary - 316:24
67Mega-Recitation 1_ Rule-Based Systems - 115:42- 68Mega-Recitation 1_ Rule-Based Systems - 215:46
- 69Mega-Recitation 1_ Rule-Based Systems - 315:42
70Mega-Recitation 2_ Basic Search, Optimal Search - 117:21- 71Mega-Recitation 2_ Basic Search, Optimal Search - 217:23
- 72Mega-Recitation 2_ Basic Search, Optimal Search - 317:17
73Mega-Recitation 3_ Games, Minimax, Alpha-Beta - 117:01- 74Mega-Recitation 3_ Games, Minimax, Alpha-Beta - 217:02
- 75Mega-Recitation 3_ Games, Minimax, Alpha-Beta - 316:56
76Mega-Recitation 4_ Neural Nets - 117:35- 77Mega-Recitation 4_ Neural Nets - 217:37
- 78Mega-Recitation 4_ Neural Nets - 317:32
79Mega-Recitation 5_ Support Vector Machines - 116:40- 80Mega-Recitation 5_ Support Vector Machines - 216:41
- 81Mega-Recitation 5_ Support Vector Machines - 316:39
82Mega-Recitation 6_ Boosting - 116:41- 83Mega-Recitation 6_ Boosting - 216:44
- 84Mega-Recitation 6_ Boosting - 316:41
85Mega-Recitation 7_ Near Misses, Arch Learning - 111:04- 86Mega-Recitation 7_ Near Misses, Arch Learning - 211:06
- 87Mega-Recitation 7_ Near Misses, Arch Learning - 310:59
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