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Information

  • 授课对象: 计算机系本科生
  • 教室: 仙林校区仙 I-103
  • 时间: 8:00-10:00
  • 教材: Stuart J. Russell, Peter Norvig. Artificial Intelligence: A Modern Approach (3rd edition), Pearson, 2011.
  • 助教: 杨杨
  • 总评: 课程作业 + 期末考试
  • 课程讨论QQ群：168762353
  • 考试:

相关课程

建议同时选修“机器学习”、“数据挖掘”、“模式识别”课

作业

本次课程有四次作业，将基于GVGAI框架，请立即开始熟悉该框架：http://www.gvgai.net

课程材料

  1. Introduction (PDF)
  2. Search 1: Uninformed Search (PDF)
  3. Search 2: Informed Search (PDF)
  4. Search 3: Adversarial Search (PDF)
  5. Search 4: Beyond Classical Search: Bandit, Monte-Carlo Tree Search, General Search and CSP (PDF)
  6. Knowledge 1: Propositional Logic
  7. Knowledge 2: First Order Logic
  8. Knowledge 3: SAT, Planning and Ontology
  9. Uncertainty 1: Probability & Bayesian Network
  10. Uncertainty 2: Inference in Bayesian Network
  11. Learning 1: Supervised Learning & Decision Trees
  12. Learning 2: Neural Networks
  13. Learning 3: Principles of Supervised Learning
  14. Learning 4: Linear Models
  15. Learning 5: Ensemble Learners
  16. Learning 6: Feature Processing
  17. Learning 7: Deep Learning
  18. Learning 8: Reinforcement learning
  19. Final: On Artificial Intelligence

slides are derived from Russell's in http://aima.cs.berkeley.edu/instructors.html

学术资源

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