Overview

This course is about the theory and practice of Artificial Intelligence. We will study modern techniques for computers to represent task-relevant information and make intelligent (i.e. satisficing or optimal) decisions towards the achievement of goals. The search and problem solving methods are applicable throughout a large range of industrial, civil, medical, financial, robotic, and information systems. We will investigate questions about AI systems such as: how to represent knowledge, how to effectively generate appropriate sequences of actions and how to search among alternatives to find optimal or near-optimal solutions. We will also explore how to deal with uncertainty in the world and how to learn from experience. We expect that by the end of the course students will have a thorough understanding of the algorithmic foundations of AI, how probability and AI are closely interrelated, and how automated agents learn. We also expect students to acquire a strong appreciation of the big-picture aspects of developing fully autonomous intelligent agents.

Prerequisites

There are no formal pre-requisites for the course, but students should have previous programming experience (programming assignments will be given in Python), as well as some general CS background. Please see the instructors if you are unsure whether your background is suitable for the course.

Office Hours

Name	Email	Hours	Location
Emma Brunskill	ebrunskill@cs.cmu.edu	Thursdays 11am-12 (except Oct 20)	GHC 7217
Ariel Procaccia	arielpro@cs.cmu.edu	Fridays 4:00-5:00	GHC 7002
Gianni Di Caro	gdicaro@cmu.edu	Mondays 1:30-2:30pm	GHC 7007
Zhaohan (Daniel) Guo	zguo@cs.cmu.edu	Wednesdays 4:30-5:30pm	GHC 8127
Nicolas Resch	nresch@cs.cmu.edu	Tuesdays 11am-12	GHC 7507
Andrew Pratt	aspratt@andrew.cmu.edu	Mondays 5-6pm	GHC 5 Teaching Commons Table 4
Klas Leino	kleino@andrew.cmu.edu	Thursdays 4-5pm	GHC 7004

Draft Schedule (Subject to change)

Dates (AB, C)	Topic	Instructor (AB,C)	Slides (AB, C)	Video	Reference
8/29, 8/30	Introduction	Brunskill+Procaccia, Di Caro	AB-pdf, C-pdf	AB-video
8/31, 9/1	Uninformed Search	Procaccia, Di Caro	AB-pdf, C-pdf	AB-video	R&N Ch. 3
9/5, 9/6	Local Search (Section C) Labor Day (Sections A/B)	N/A, Di Caro	C-pdf
9/7, 9/8	Constraint Satisfaction Problems	Brunskill, Di Caro	AB-pdf, C-pdf	AB-video	R&N Ch. 6
9/12, 9/13	Informed Search	Procaccia, Di Caro	AB-pdf, C-pdf	AB-video	R&N Ch. 3
9/14, 9/15	Motion Planning + Classical Planning 1	Procaccia, Di Caro	AB-pdf, C-pdf	AB-video	R&N Ch. 10, 25
9/19, 9/20	Classical Planning 2	Procaccia, Di Caro	AB-pdf, C-pdf	AB-video	R&N Ch. 10
9/21, 9/22	Probability (AB) / Classical Planning (C)	Procaccia, Di Caro	AB-pdf, C-pdf	AB-video
9/26, 9/27	Bayesian Networks 1	Brunskill, Di Caro	AB-pdf, C-pdf	AB-video	R&N Ch. 14
9/28, 9/29	Bayesian Networks 2	Brunskill, Di Caro	AB-pdf, C-pdf	AB-video	R&N Ch. 14
10/3, 10/4	Markov Decision Processes	Brunskill, Di Caro	AB-pdf, C-pdf	AB-video	R&N Ch. 17
10/5, 10/6	Reinforcement Learning 1	Brunskill, Di Caro	AB-pdf, C-pdf	AB-video	R&N Ch. 21
10/10, 10/11	Reinforcement Learning 2	Brunskill, Di Caro	AB-pdf, C-pdf	AB-video	R&N Ch. 21
10/12, 10/13	Convex Optimization	Procaccia, Di Caro	AB-pdf, C-pdf	AB-video
10/17, 10/18	Integer Programming	Procaccia, Di Caro	AB-pdf, C-pdf	AB-video
10/19, 10/20	Midterm Exam
10/24, 10/25	Machine Learning 1	Brunskill, Di Caro	AB-pdf, C-pdf	AB-video
10/26, 10/27	Machine Learning 2	Brunskill, Di Caro	AB-pdf, C-pdf	AB-video
10/31, 11/1	Deep Learning	Brunskill, Di Caro	AB-pdf, C-pdf	AB-video
11/2, 11/3	Vision	Brunskill, Di Caro	AB-pdf, C-pdf	AB-video
11/7, 11/8	Natural Language Processing	Berg-Kirkpatrick, NA	ABC-pdf	AB-video
11/9, 11/10	Social Choice	Procaccia, Procaccia	ABC-pdf	AB-video	S&LB Ch. 9
11/14, 11/15	Multi-Robot Systems	Di Caro, Di Caro	ABC-pdf	AB-video
11/16, 11/17	Game Theory 1	Procaccia, Di Caro	AB-pdf, C-pdf	AB-video	S&LB Ch. 3
11/21, 11/22	Game Theory 2	Procaccia, Di Caro	AB-pdf, C-pdf	AB-video
11/23, 11/24	Thanksgiving
11/28, 11/29	Game theory 3	Procaccia, Di Caro	AB-pdf, C-pdf	AB-video	S&LB Ch. 5
11/30, 12/1	Swarm Intelligence 1	Di Caro, Di Caro	AB-pdf, C-pdf	AB-video
12/5, 12/6	Swarm Intelligence 2	Di Caro, Di Caro	AB-pdf, C-pdf	AB-video
12/7	Poster session	NA

Assignments

There will be five assignments: they will involve both written answers and programming assignments. Written questions will involve working through algorithms presented in the class, deriving and proving mathematical results, and critically analyzing the material presented in class. Programming assignments will involve writing code in Python to implement various algorithms presented in class.

15-781 Project

Students enrolled in 15-781 will also complete a course project. Late days may not be used on the course project.

Homework Policies

• Homework is due on autolab by the posted deadline. Assignments submitted past the deadline will incur the use of late days.

• You have 6 late days, but cannot use more than 2 late days per homework. No credit will be given for homework submitted more than 2 days after the due date. After your 6 late days have been used you will receive 20% off for each additional day late.

• You can discuss the exercises with your classmates, but you should write up your own solutions. If you find a solution in any source other than the material provided on the course website or the textbook, you must mention the source. You can work on the programming questions in pairs, but theoretical questions are always submitted individually. Make sure that you include a README file with your andrew id and your collaborator's andrew id.

Exams

The class includes both a midterm and final exam. The material for the midterm includes all lectures up to (and including) the Reinforcement Learning 2 lecture.

Exam	Without Solutions	With Solutions
Practice Midterm	pdf	pdf
Practice final	pdf	pdf