Introduction to Artificial Intelligence

Lecture Topic
| Readings in AIMA | Assignments | ||

Jan | 21 | Introduction, history | Chapter 1 | A0 (Lisp) |

23 | Intelligent agents | Chapter 2 | ||

26 | Intelligent agents contd. | |||

28 | Problem solving, uninformed search | Chapter 3 | A0 due, A1 (Search) | |

30 | A* search and heuristic functions | Chapter 4.1-4.2 | ||

Feb | 2 | Local search | Chapter 4.3-4.4 | |

4 | Online search | Chapter 4.5 | ||

6 | Constraint satisfaction | Chapter 5.1-5.2 | ||

9 | Constraint satisfaction contd. | Chapter 5.3-5.4 | ||

11 | Game-playing | Chapter 6.1-6.3 | A1 due | |

13 | Game-playing contd. | Chapter 6.4-6.7 | ||

16 | Presidents' Day Holiday | Anyone Can be An Expert Skier 2: Powder, Bumps, and Carving | A2 (Local search, CSPs) | |

18 | Logical agents; propositional logic | Chapter 7.1-7.4 | ||

20 | Inference in propositional logic | Chapter 7.5-7.7 | ||

23 | First-order logic | Chapter 8.1-8.3 | ||

25 | Inference in first-order logic | Chapter 9.1-9.2 | ||

27 | Inference contd., logic programming | Chapter 9.3-9.4 | ||

Mar | 1 | Planning problems | Chapter 11.1-11.2 | A2 due |

3 | Partial-order planning | Chapter 11.3 | A3 (logic) | |

5 | Planning as logical inference | Chapter 11.5 | ||

8 | Midterm
| |||

10 | Uncertainty, probability | Chapter 13.1-13.4 | ||

12 | Independence, Bayes' Rule | Chapter 13.5-13.7 | A3 due | |

15 | Bayesian networks | Chapter 14.1-14.3 | ||

17 | Exact inference in Bayesian networks | Chapter 14.4 | ||

19 | Approximate inference in Bayesian networks | Chapter 14.5 | ||

22 | Spring Break | |||

24 | Spring Break | |||

26 | Spring Break | |||

29 | Temporal probability models; Hidden Markov models | Chapter 15.1-15.3 | ||

31 | Speech recognition | Chapter 15.6 | ||

Apr | 2 | Dynamic Bayesian networks | Chapter 15.5 | |

5 | Utility theory | Chapter 16.1-16.4 | ||

7 | Decision networks; value of information | Chapter 16.5-16.6 | ||

9 | Sequential decisions | Chapter 17.1 | ||

12 | Dynamic programming algorithms | Chapter 17.2-17.4 | ||

14 | Learning | Chapter 18.1-18.2 | ||

16 | Decision tree learning | Chapter 18.1-18.3 | ||

19 | Statistical learning | Chapter 20.1-20.2 | ||

21 | Learning in Bayesian networks | Chapter 20.3 | ||

23 | Neural networks | Chapter 20.5 | ||

26 | Natural language communication and syntax | Chapter 22.1-22.3 | ||

28 | Natural language semantics | Chapter 22.5-22.7 | ||

30 | Computer vision | Chapter 24 | ||

May | 3 | Robotics | Chapter 25.1-25.3 | |

5 | Robotics contd. | Chapter 25.4-25.6 | ||

7 | Philosophical issues | Chapter 26.1 | ||

10 | Philosophical issues, contd. | Chapter 26.2 | ||

17 | Final (8.00am - 11.00am) |