人工智能原理 – 北京大学 大学课程公开课慕课教程

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人工智能原理 – 北京大学 大学课程
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人工智能原理 – 北京大学 理工科
人工智能原理 – 北京大学 课程资源
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人工智能原理 – 北京大学
–8 Part IV. Planning： Chapter 8. Classic and Real-world Planning
—-8.3 Planning and Scheduling
—-8.1 Planning Problems
—-8.2 Classic Planning
—-8.4 Real-World Planning
—-8.5 Decision-theoretic Planning
–7 Part III. Reasoning： Chapter 7. Reasoning by Knowledge
—-7.2 Knowledge Representation
—-7.4 Ontological Engineering
—-7.1 Overview
—-7.5 Bayesian Networks
—-7.3 Representation using Logic
–9 Part V. Learning： Chapter 9. Perspectives about Machine Leaning
—-9.1 What is Machine Learning
—-9.2 History of Machine Learning
—-9.5 Applications and Terminologies
—-9.4 Three Perspectives on Machine Learning
—-9.3 Why Different Perspectives
–4 Part II. Searching： Chapter 4. Local Search and Swarm Intelligence
—-4.2 Local Search Algorithms
—-4.1 Overview
—-4.3 Optimization and Evolutionary Algorithms
—-4.4 Swarm Intelligence and Optimization
–6 Part II. Searching： Chapter 6. Constraint Satisfaction Problem
—-6.4 Local Search for CSPs
—-6.5 The Structure of Problems
—-6.1 Constraint Satisfaction Problems (CSPs)
—-6.2 Constraint Propagation： Inference in CSPs
—-6.3 Backtracking Search for CSPs
–5 Part II. Searching： Chapter 5. Adversarial Search
—-5.3 Alpha-Beta Pruning
—-5.2 Optimal Decisions in Games
—-5.5 Stochastic Games
—-5.1 Games
—-5.6 Monte-Carlo Methods
—-5.4 Imperfect Real-time Decisions
–12 Part V. Learning： Chapter 12. Models in Machine Learning
—-12.4 Networked Models
—-12.1 Probabilistic Models
—-12.2 Geometric Models
—-12.3 Logical Models
–3 Part II. Searching： Chapter 3. Solving Problems by Search
—-3.5 Informed Search Strategies
—-3.1 Problem Solving Agents
—-3.6 Heuristic Functions
—-3.2 Example Problems
—-3.3 Searching for Solutions
—-3.4 Uninformed Search Strategies
–10 Part V. Learning： Chapter 10. Tasks in Machine Learning
—-10.4 Ranking
—-10.5 Dimensionality Reduction
—-10.2 Regression
—-10.1 Classification
—-10.3 Clustering
–2 Part I. Basics： Chapter 2. Intelligent Agent
—-2.5 Category of Intelligent Agents
—-2.2 Rational Agents
—-2.3 Task Environments
—-2.4 Intelligent Agent Structure
—-2.1 Approaches for Artificial Intelligence
–1 Part I. Basics： Chapter 1. Introduction
—-1.4 The State of The Art
—-1.1 Overview of Artificial Intelligence
—-1.2 Foundations of Artificial Intelligence
—-1.3 History of Artificial Intelligence
–11 Part V. Learning： Chapter 11. Paradigms in Machine Learning
—-11.3 Reinforcement Learning Paradigm
—-11.2 Unsupervised Learning Paradigm
—-11.1 Supervised Learning Paradigm
—-11.4 Other Learning Paradigms
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