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Information

  • 授课对象: 计算机系本科生(一班)
  • 教室: 仙林校区仙2-104
  • 时间: 周五5-6节
  • 教材: Stuart J. Russell, Peter Norvig. Artificial Intelligence: A Modern Approach (3rd edition), Pearson, 2011.
  • 助教: 胡毅奇
  • 总评: 课程作业 + 期末考试
  • 课程讨论QQ群:513053106
  • 考试: 6月26日 16:30-18:30 仙1-319

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相关课程

建议同时选修“机器学习”、“数据挖掘”、“模式识别”课

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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

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课程材料

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

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

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学术资源


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