Artificial Intelligence (COMPSCI 570), Fall 2021

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 online, we also have the following rooms:
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: Artificial Intelligence: A Modern Approach, Stuart Russell and Peter Norvig.

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 not need to have taken an undergraduate AI course (though of course it will help if you have).

Grading (tentative)
Assignments: 35%
Midterm exams: 30%
Final exam: 30%
Participation: 5%

Rules for assignments (not the exams): You must show all your work, even if this is not specifically indicated. You may discuss assignments with at most one other person. Each person must do her or his own writeup, and at that point derive the solution on her/his own. You may present things to each other on a (virtual) whiteboard, but you may not copy anything from the whiteboard, or take screenshots, or take notes during the meeting, or anything like that. The only thing you should take from the meeting is what you remember from it. This also implies that you cannot copy any code from each other. Copying code is considered a serious form of cheating, and there are sophisticated ways of detecting copied code. You should always acknowledge your homework partner (if any), as well as all other sources, on the writeup.

Rules for takehome exams: You must show all your work, even if this is not specifically indicated. You may use all the materials from the course website (slides, the book, also your homework, etc.). You may not work with any other person, or access the rest of the Internet. Do not communicate with anyone else about the exam (until everyone has turned them in). If you break these rules you will run the risk of being classified as cheating. If you feel you may have accidentally gotten input that you should not have had, you must report it immediately and proactively to not run the risk of being classified as cheating.

Struggling / cheating: Sometimes, students fall behind, for example due to personal difficulties. These days, there are many additional challenges. You should try hard to keep up, because it will be easiest to learn the material that way; but if you are struggling, please reach out to the teaching team as soon as possible. We will be very understanding about difficult situations. But, do not get tempted to break the rules. Attempting to take advantage of the generally difficult situation by cheating is wrong, and hurts other students. We will pursue any and all cheating cases to the full extent. You should be aware that we will pursue various ways to detect cheating, some of them noticeable and some of them not noticeable. You may find that something in an assignment or exam occasionally looks a bit funny. You do not need to worry about this, and you also do not need to worry if you do not see anything like that -- as long as you are following the rules.

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.
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
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).