Written by a well-known computer science education and researcher. No previous knowledge of ML or functional programming is assumed.This is the first book that offers BOTH a highly accessible, step-by-step introductory tutorial on ML programming and a complete reference to, and explanation of, advanced features. The author uses a wide variety of digestible program examples to bring the reader along at a reasonable pace. More sophisticated programs and advanced concept topics balance out a book that is usable in a number of courses and settings for either self-study or class discussion.
I. ARTIFICIAL INTELLIGENCE.
2. Intelligent Agents.
3. Solving Problems by Searching.
4. Informed Search and Exploration.
5. Constraint Satisfaction Problems.
6. Adversarial Search.
III. KNOWLEDGE AND REASONING.
7. Logical Agents.
8. First-Order Logic.
9. Inference in First-Order Logic.
10. Knowledge Representation.
12. Planning and Acting in the Read World.
V. UNCERTAIN KNOWLEDGE AND REASONING.
14. Probabilistic Reasoning Systems.
15. Probabilistic Reasoning Over Time.
16. Making Simple Decisions.
17. Making Complex Decisions.
18. Learning from Observations.
19. Statistical Learning.
20. Reinforcement Learning.
21. Knowledge in Learning.
VII. COMMUNICATING, PERCEIVING, AND ACTING.
22. Agents that Communicate.
23. Text Processing in the Large.
26. Philosophical Foundations.
27. AI: Present and Future.