WF 10:15-11:30am. (Final exam: December 8, 9am-noon.)

We may sometimes also have helper sessions on Monday, at the same time.

Location: while the course will begin

M 10:15AM - 11:30AM: Social Sciences 136 (that's only for helper sessions)

W 10:15AM - 11:30AM: Wilkinson Auditorium 021

F 10:15AM - 11:30AM: Gross Hall 103

Instructor: Vincent Conitzer (please call me Vince).

Office hours: immediately after class (WF 11:30AM - 12:30PM)

Teaching Assistants: Juncheng Dong, Yingfan Wang, Diane Hu.

Teaching assistants' OH:

Tuesday: 3:30pm-5:00pm (LINK group study 8 (lower level of the Perkins library))

Thursday: 8:15pm-9:45pm (online, check Ed Discussion for link)

Textbook:

comfortable programming in a general-purpose programming language

some knowledge of algorithmic concepts such as running times of algorithms; having at least a rough idea of what NP-hard means

some familiarity with probability (we will go over this from the beginning but we will cover the basics only briefly)

not scared of mathematics, some background in discrete mathematics, able to do simple mathematical proofs

If you do not have a standard undergraduate computer science background, the course may still be appropriate for you, but talk to me first. Well-prepared undergraduates are certainly welcome.

You do

Assignments: 35%

Midterm exams: 30%

Final exam: 30%

Participation: 5%

We will be flexible with the schedule. Each topic will probably take a number of lectures to finish.

Sometimes, a book chapter will include more information than what we cover in class; in those cases, for the purpose of exams, you are only responsible for what we covered in class.

Date |
Topic |
Materials |

8/24, 8/26 | Introduction. | Chapter 1. Introduction slides: ppt, pdf. Homework 0. Optional:Article about broader concerns about AI. Winograd schema example on Google Translate. |

8/26-9/15 | Search. Constraint satisfaction and optimization problems. | Chapters 3, 4, 6.
Search slides: ppt, pdf. More search slides: ppt, pdf. For more about linear and integer programming, you can go to the website of a course I taught recently; especially the introduction and branch and bound lecture notes might be useful. MLB scheduling blog post. Homework 1. Helper files: knight distances, queens file, tile puzzle file 1, tile puzzle file 2. Additional test cases: input_FPK_1.txt, output_FPK_1.txt, input_FPK_2.txt, output_FPK_2.txt, output_SQP_1_(size_18).txt, output_SQP_2_(size_24).txt. |

9/17-9/22 | Game playing. | Chapter 5. Slides: ppt, pdf. Homework 2. |

9/22-10/6 | Logic. | Chapters 7, 8,
9.
Propositional logic slides: ppt, pdf. First-order logic slides: ppt, pdf. Homework 3. |

10/8, 10/13, 10/15 | Planning. |
Chapter 10 (maybe), 11. Or you might
appreciate this
version especially for partial order planning. Planning slides: ppt, pdf. Homework 4, which uses the helper code from hw4_helper_code.zip. |

10/11 | Midterm review/practice. | Practice midterm. Pictures from the review session (solutions): search tree, propositional logic, first-order logic. Full solution in recording on Ed Discussion (same place as other recordings). |

10/15 - 11/3 | Probabilistic reasoning. | Chapters 12-14. Probability slides: ppt, pdf. Bayes nets slides: ppt, pdf. Markov processes and HMMs slides: ppt, pdf. Homework 5. |

11/15 | Midterm review/practice. | Practice midterm. Pictures from the review session (solutions): Bayes nets, planning. Full solution in recording on Ed Discussion (same place as other recordings). |

11/5-11/19 | Decision theory. Markov decision processes, POMDPs. Game theory. | Chapters 16, 17
. Decision theory slides: ppt, pdf. MDP/POMDP slides: ppt, pdf. Game theory slides: ppt, pdf. Homework 6. |

11/29 | Final review/practice. | Practice final exam. Pictures from the review session (solutions): true or false, search, planning, HMM, MDP. Full solution in recording on Ed Discussion (same place as other recordings). |