人工智能原理
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1.1 Overview of Artificial Intelligence (人工智能概述)(09:11)
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1.2 Foundations of Artificial Intelligence(人工智能基础)(16:52)
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1.3 History of Artificial Intelligence(人工智能历史)(22:36)
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1.4 The State of Artificial Intelligence(人工智能现状)(21:16)
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2.1 Approaches for Artificial Intelligence(人工智能研究途(11:44)
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2.2 Rational Agents (理性主体)(09:06)
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2.3 Task Environments (任务环境)(07:09)
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2.4 Intelligent Agent Structure (Agent的结构)(07:54)
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2.5 Category of Intelligent Agents(Agent的分类)(17:27)
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3.1 Problem Solving Agents(问题求解Agent)(10:13)
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3.2 Example Problems(问题实例)(15:45)
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3.3 Searching for Solutions(通过搜索求解)(07:07)
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3.4 Uninformed Search Strategies(无信息搜索策略)(11:23)
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3.5 Informed Search Strategies(有信息搜索策略)(09:51)
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3.6 Heuristic Functions(启发式函数)(05:25)
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4.1 Overview(概述)(06:56)
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4.2 Local Search Algorithms(局部搜索算法)(13:38)
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4.3 Optimization and Evolutionary Algorithms (优化和进(16:16)
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4.4 Swarm Intelligence and Optimization(群体智能和优化)(14:54)
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5.1 Games(博弈)(19:33)
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5.2 Optimal Decisions in Games(博弈中的优化决策)(13:01)
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5.3 Alpha-Beta Pruning(Alpha-Beta剪枝)(08:23)
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5.4 Imperfect Real-time Decisions(不完美的实时决策)(05:38)
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5.5 Stochastic Games(随机博弈)(07:59)
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5.6 Monte-Carlo Methods(蒙特卡洛方法)(16:14)
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6.1 Constraint Satisfaction Problems (约束满足问题)(30:13)
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6.2 Constraint Propagation: Inference in CSPs(约束传播(13:35)
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6.3 Backtracking Search for CSPs(CPS的回溯搜索)(13:26)
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6.4 Local Search for CSPs(CPS局部搜索)(06:42)
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6.5 The Structure of Problems(问题的结构)(07:41)
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7.1 Overview(概述)(08:32)
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7.2 Knowledge Representation(知识表示)(15:45)
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7.3 Representation using Logic(逻辑表示)(25:52)
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7.4 Ontological Engineering(本体工程)(14:15)
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7.5 Bayesian Networks(贝叶斯网络)(36:06)
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8.1 Planning Problems(规划问题)(18:45)
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8.2 Classic Planning(经典规划)(24:58)
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8.3 Planning and Scheduling(规划与调度)(07:15)
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8.4 Real-World Planning(现实世界规划)(18:31)
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8.5 Decision-theoretic Planning(决策理论规划)(15:55)
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9.1 What is Machine Learning(什么是机器学习)(21:08)
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9.2 History of Machine Learning(机器学习的历史)(12:20)
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9.3 Why Different Perspectives(为什么需要不同的视角)(07:40)
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9.4 Three Perspectives on Machine Learning(机器学习的三个(28:48)
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9.5 Applications and Terminologies(机器学习的应用及有关术语)(20:31)
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10.1 Classification(分类)(40:15)
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10.2 Regression(回归)(19:01)
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10.3 Clustering(聚类)(27:17)
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10.4 Ranking(排名)(09:51)
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10.5 Dimensionality Reduction(降维)(11:55)
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11.1 Supervised Learning Paradigm(有监督学习范式)(47:43)
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11.2 Unsupervised Learning Paradigm(无监督学习范式)(27:35)
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11.3 Reinforcement Learning Paradigm(强化学习范式)(33:51)
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11.4 Other Learning Paradigms(其他学习范式)(17:47)
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12.1 Probabilistic Models(概率模型)(35:01)
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12.2 Geometric Models(几何模型)(21:58)
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12.3 Logical Models(逻辑模型)(11:27)
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12.4 Networked Models(网络模型)(46:44)
2021-01-17 15:22:40
好
2021-01-04 09:23:39