学堂在线 × 王文敏教授- 人工智能原理与核心算法

📁 人工智能原理
📁 {3}–Part II Searching Chapter 3 Solv
📁 {6}–Part II Searching Chapter 6 Cons
📁 {12}–Part V Learning Chapter 12 Model
📁 {1}–Part I Basics Chapter 1 Introduc
📁 {10}–Part V Learning Chapter 10 Tasks
📁 {2}–Part I Basics Chapter 2 Intellig
📁 {9}–Part V Learning Chapter 9 Perspe
📁 {8}–Part IV Planning Chapter 8 Class
📁 {11}–Part V Learning Chapter 11 Parad
📁 {7}–Part III Reasoning Chapter 7 Rea
📁 {4}–Part II Searching Chapter 4 Loca
📁 {5}–Part II Searching Chapter 5 Adve
📁 {6}–6.6 Summary（小结）
📁 {3}–6.3 Backtracking Search for CSPs
📁 {2}–6.2 Constraint Propagation Infer
📁 {5}–6.5 The Structure of Problems（问题
📁 {4}–6.4 Local Search for CSPs（CPS局部搜
📁 {1}–6.1 Constraint Satisfaction Prob
📁 {1}–1.1 Overview of Artificial Intel
📁 {5}–1.5 Summary (小结)
📁 {4}–1.4 The State of Artificial Inte
📁 {2}–1.2 Foundations of Artificial In
📁 {3}–1.3 History of Artificial Intell
📁 {5}–12.5 Summary（小结）
📁 {1}–12.1 Probabilistic Models（概率模型）
📁 {4}–12.4 Networked Models（网络模型）
📁 {3}–12.3 Logical Models（逻辑模型）
📁 {2}–12.2 Geometric Models（几何模型）
📁 {2}–3.2 Example Problems（问题实例）
📁 {4}–3.4 Uninformed Search Strategies
📁 {6}–3.6 Heuristic Functions（启发式函数）
📁 {5}–3.5 Informed Search Strategies（有
📁 {1}–3.1 Problem Solving Agents（问题求解A
📁 {3}–3.3 Searching for Solutions（通过搜索
📁 {7}–3.7 Summary（小结）
📁 {5}–9.5 Applications and Terminologi
📁 {1}–9.1 What is Machine Learning（什么是
📁 {2}–9.2 History of Machine Learning（
📁 {3}–9.3 Why Different Perspectives（为
📁 {4}–9.4 Three Perspectives on Machin
📁 {6}–9.6 Summary（小结）
📁 {3}–10.3 Clustering（聚类）
📁 {4}–10.4 Ranking（排名）
📁 {2}–10.2 Regression（回归）
📁 {5}–10.5 Dimensionality Reduction（降维
📁 {6}–10.6 Summary（小结）
📁 {1}–10.1 Classification（分类）
📁 {3}–2.3 Task Environments (任务环境）
📁 {1}–2.1 Approaches for Artificial In
📁 {6}–2.6 Summary（小结）
📁 {4}–2.4 Intelligent Agent Structure
📁 {2}–2.2 Rational Agents (理性主体)
📁 {5}–2.5 Category of Intelligent Agen
📁 {3}–8.3 Planning and Scheduling（规划与调
📁 {4}–8.4 Real-World Planning（现实世界规划）
📁 {5}–8.5 Decision-theoretic Planning（
📁 {1}–8.1 Planning Problems（规划问题）
📁 {6}–8.6 Summary（小结）
📁 {2}–8.2 Classic Planning（经典规划）
📁 {4}–7.4 Ontological Engineering（本体工程
📁 {3}–7.3 Representation using Logic（逻
📁 {5}–7.5 Bayesian Networks（贝叶斯网络）
📁 {2}–7.2 Knowledge Representation（知识表
📁 {6}–7.6 Summary（小结）
📁 {1}–7.1 Overview（概述）
📁 {4}–5.4 Imperfect Real-time Decision
📁 {7}–5.7 Summary（小结）
📁 {1}–5.1 Games（博弈）
📁 {2}–5.2 Optimal Decisions in Games（博
📁 {5}–5.5 Stochastic Games（随机博弈）
📁 {3}–5.3 Alpha-Beta Pruning（Alpha-Bet
📁 {6}–5.6 Monte-Carlo Methods（蒙特卡洛方法）
📁 {4}–4.4 Swarm Intelligence and Optim
📁 {1}–4.1 Overview（概述）
📁 {2}–4.2 Local Search Algorithms（局部搜索
📁 {3}–4.3 Optimization and Evolutionar
📁 {5}–4.5 Summary（小结）
📁 {3}–11.3 Reinforcement Learning Para
📁 {1}–11.1 Supervised Learning Paradig
📁 {4}–11.4 Other Learning Paradigms（其他
📁 {5}–11.5 Summary（小结）
📁 {2}–11.2 Unsupervised Learning Parad
📄 [6.2.1]–6.2 Constraint Propagation Infer.mp4
📄 (6.2.1)–asset-v1_PekingX+20180320001+201.pdf
📄 (6.3.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [6.3.1]–6.3 Backtracking Search for CSPs.mp4
📄 (6.5.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [6.5.1]–6.5 The Structure of Problems（问题.mp4
📄 #6.6.1#–html.pdf
📄 (6.4.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [6.4.1]–6.4 Local Search for CSPs（CPS局部搜.mp4
📄 (6.1.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [6.1.1]–6.1 Constraint Satisfaction Prob.mp4
📄 #1.5.1#–html.pdf
📄 (1.1.1)–asset-v1_PekingX+20180320001+201.pdf
📄 (1.3.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [1.3.1]–1.3 History of Artificial Intell.mp4
📄 (1.4.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [1.4.1]–1.4 The State of Artificial Inte.mp4
📄 #12.5.1#–html.pdf
📄 [1.2.1]–1.2 Foundations of Artificial In.mp4
📄 (1.2.1)–asset-v1_PekingX+20180320001+201.pdf
📄 (12.3.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [12.3.1]–12.3 Logical Models（逻辑模型）.mp4
📄 (12.2.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [12.2.1]–12.2 Geometric Models（几何模型）.mp4
📄 (12.1.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [12.1.1]–12.1 Probabilistic Models（概率模型）.mp4
📄 (12.4.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [12.4.1]–12.4 Networked Models（网络模型）.mp4
📄 (3.5.1)–asset-v1_PekingX+20180320001+201.pdf
📄 (3.5.2)–asset-v1_PekingX+20180320001+201.pdf
📄 [3.5.1]–3.5 Informed Search Strategies（有.mp4
📄 [3.5.2]–3.5 Informed Search Strategies（有.mp4
📄 [3.6.1]–3.6 Heuristic Functions（启发式函数）.mp4
📄 (3.6.1)–asset-v1_PekingX+20180320001+201.pdf
📄 (3.4.4)–asset-v1_PekingX+20180320001+201.pdf
📄 [3.4.4]–3.4 Uninformed Search Strategies.mp4
📄 (3.4.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [3.4.1]–3.4 Uninformed Search Strategies.mp4
📄 [3.4.5]–3.4 Uninformed Search Strategies.mp4
📄 [3.4.6]–3.4 Uninformed Search Strategies.mp4
📄 (3.4.3)–asset-v1_PekingX+20180320001+201.pdf
📄 (3.4.6)–asset-v1_PekingX+20180320001+201.pdf
📄 (3.4.5)–asset-v1_PekingX+20180320001+201.pdf
📄 (3.4.2)–asset-v1_PekingX+20180320001+201.pdf
📄 [3.4.3]–3.4 Uninformed Search Strategies.mp4
📄 [3.4.2]–3.4 Uninformed Search Strategies.mp4
📄 [3.2.1]–3.2 Example Problems（问题实例）.mp4
📄 (3.2.1)–asset-v1_PekingX+20180320001+201.pdf
📄 (3.3.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [3.3.1]–3.3 Searching for Solutions（通过搜索.mp4
📄 [9.5.1]–9.5 Applications and Terminologi.mp4
📄 (9.5.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [3.1.1]–3.1 Problem Solving Agents（问题求解A.mp4
📄 (3.1.1)–asset-v1_PekingX+20180320001+201.pdf
📄 #3.7.1#–html.pdf
📄 [9.2.1]–9.2 History of Machine Learning（.mp4
📄 (9.2.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [9.3.1]–9.3 Why Different Perspectives（为.mp4
📄 (9.3.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [9.1.1]–9.1 What is Machine Learning（什么是.mp4
📄 (9.1.1)–asset-v1_PekingX+20180320001+201.pdf
📄 (9.4.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [9.4.1]–9.4 Three Perspectives on Machin.mp4
📄 #9.6.1#–html.pdf
📄 [10.2.1]–10.2 Regression（回归）.mp4
📄 (10.2.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [10.3.1]–10.3 Clustering（聚类）.mp4
📄 (10.3.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [10.4.1]–10.4 Ranking（排名）.mp4
📄 (10.4.1)–asset-v1_PekingX+20180320001+201.pdf
📄 #10.6.1#–html.pdf
📄 [10.1.1]–10.1 Classification（分类）.mp4
📄 (10.1.1)–asset-v1_PekingX+20180320001+201.pdf
📄 (2.3.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [2.3.1]–2.3 Task Environments (任务环境）.mp4
📄 [10.5.1]–10.5 Dimensionality Reduction（降维.mp4
📄 (10.5.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [2.4.1]–2.4 Intelligent Agent Structure.mp4
📄 (2.4.1)–asset-v1_PekingX+20180320001+201.pdf
📄 (2.1.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [2.1.1]–2.1 Approaches for Artificial In.mp4
📄 [2.2.1]–2.2 Rational Agents (理性主体).mp4
📄 (2.2.1)–asset-v1_PekingX+20180320001+201.pdf
📄 #2.6.1#–html.pdf
📄 (8.3.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [8.3.1]–8.3 Planning and Scheduling（规划与调.mp4
📄 (8.5.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [8.5.1]–8.5 Decision-theoretic Planning（.mp4
📄 (8.4.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [8.4.1]–8.4 Real-World Planning（现实世界规划）.mp4
📄 [2.5.1]–2.5 Category of Intelligent Agen.mp4
📄 (2.5.1)–asset-v1_PekingX+20180320001+201.pdf
📄 (8.2.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [8.2.1]–8.2 Classic Planning（经典规划）.mp4
📄 (7.4.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [7.4.1]–7.4 Ontological Engineering（本体工程.mp4
📄 [8.1.1]–8.1 Planning Problems（规划问题）.mp4
📄 (8.1.1)–asset-v1_PekingX+20180320001+201.pdf
📄 #8.6.1#–html.pdf
📄 (7.3.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [7.3.1]–7.3 Representation using Logic（逻.mp4
📄 (7.5.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [7.5.1]–7.5 Bayesian Networks（贝叶斯网络）.mp4
📄 [7.2.1]–7.2 Knowledge Representation（知识表.mp4
📄 (7.2.1)–asset-v1_PekingX+20180320001+201.pdf
📄 #7.6.1#–html.pdf
📄 [7.1.1]–7.1 Overview（概述）.mp4
📄 (7.1.1)–asset-v1_PekingX+20180320001+201.pdf
📄 (5.1.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [5.1.1]–5.1 Games（博弈）.mp4
📄 (5.4.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [5.4.1]–5.4 Imperfect Real-time Decision.mp4
📄 #5.7.1#–html.pdf
📄 (5.5.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [5.5.1]–5.5 Stochastic Games（随机博弈）.mp4
📄 (5.3.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [5.3.1]–5.3 Alpha-Beta Pruning（Alpha-Bet.mp4
📄 (5.2.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [5.2.1]–5.2 Optimal Decisions in Games（博.mp4
📄 (5.6.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [5.6.1]–5.6 Monte-Carlo Methods（蒙特卡洛方法）.mp4
📄 (4.4.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [4.4.1]–4.4 Swarm Intelligence and Opti.mp4
📄 [4.2.1]–4.2 Local Search Algorithms（局部搜索.mp4
📄 [4.2.2]–4.2 Local Search Algorithms（局部搜索.mp4
📄 (4.2.3)–asset-v1_PekingX+20180320001+201.pdf
📄 (4.2.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [4.2.3]–4.2 Local Search Algorithms（局部搜索.mp4
📄 (4.2.2)–asset-v1_PekingX+20180320001+201.pdf
📄 [4.3.1]–4.3 Optimization and Evolutionar.mp4
📄 (4.3.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [4.1.1]–4.1 Overview（概述）.mp4
📄 (4.1.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [11.3.1]–11.3 Reinforcement Learning Para.mp4
📄 (11.3.1)–asset-v1_PekingX+20180320001+201.pdf
📄 #4.5.1#–html.pdf
📄 (11.4.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [11.4.1]–11.4 Other Learning Paradigms（其他.mp4
📄 [11.1.1]–11.1 Supervised Learning Paradig.mp4
📄 (11.1.1)–asset-v1_PekingX+20180320001+201.pdf
📄 (11.2.1)–asset-v1_PekingX+20180320001+201.pdf
📄 [11.2.1]–11.2 Unsupervised Learning Parad.mp4
📄 #11.5.1#–html.pdf