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  • 授课对象: 计算机系本科生
  • 教室: 仙林校区仙2-117
  • 时间: 周五3-4节 + 双周周三1-2节
  • 教材: Stuart J. Russell, Peter Norvig. Artificial Intelligence: A Modern Approach (3rd edition), Pearson, 2011.
  • 助教: 杨敬文
  • 总评: 课程作业 + 期末考试

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作业


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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: Iterative-Improvement Methods (Download PDF)
  5. Search 4: Adversarial 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, Ontology (Download PDF)
  10. Uncertainty 1: Probability and Bayesian Network (Download PDF)
  11. Uncertainty 2: Inference in Bayesian Network (Download PDF)
  12. Uncertainty 3: Inference with Time (Download PDF)
  13. Learning 1: Decision Tree Learning (Download PDF)
  14. Learning 2: Neural Networks (Download PDF)
  15. Learning 3: Learning Principle (Download PDF)
  16. Learning 4: Linear Models (Download PDF)
  17. Learning 5: Nearest Neighbors, Naive Bayes, and Ensemble Learning (Download PDF)
  18. Learning 6: Feature Processing (Download PDF)

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

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学术资源


The end