Overview

This course is targeted at graduate students who are interested in learning about artificial intelligence. The focus is on modern AI techniques. The course also covers techniques from the intersection of AI and other disciplines such as integer programming, continuous optimization, and game theory. The course content is profiled so as to not have too much overlap with narrower specialized AI courses offered at CMU.

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
J. Zico Kolter	zkolter@cs.cmu.edu	By appointment	GHC 7115
Ariel Procaccia	arielpro@cs.cmu.edu	By appointment	GHC 7002
Brandon Amos	bamos@andrew.cmu.edu	Wed 12-1pm	GHC 9221
Shayan Doroudi	shayand@cs.cmu.edu	Mon 3-4pm	GHC 8127
Anson Kahng	akahng@cs.cmu.edu	Thurs 2-3pm	GHC 6207
Kijung Shin	kijungs@andrew.cmu.edu	Tue 4-5pm	GHC 9005

To schedule an appointment with Prof. Kolter or Prof. Procaccia, please send an email with your availability, as well as the topics you would like to discuss (e.g., specific homework problems or lectures).

Draft Schedule (Subject to change)

Date	Topic	Instructor	Slides	Video
1/16	MLK Day
1/18	Introduction	Kolter+Procaccia	slides 1	video 1
1/23	Search I	Procaccia	slides 2	video 2
1/25	Search II	Procaccia	slides 3	video 3
1/30	Optimization	Kolter	slides 4	video 4
2/1	Linear Programming	Kolter	slides 5	video 5
2/6	Integer Programming I: Theory	Kolter	slides 6	video 6
2/8	Integer Programming II: Applications	Procaccia	slides 7	video 7
2/13	Machine Learning I: Regression and Classification	Kolter	slides 8	video 8
2/15	Machine Learning II: Nonlinear Methods	Kolter	slides 9	Video 9
2/20	Computational Learning Theory	Procaccia	slides 10	Video 10
2/22	Deep Learning I: Neural Networks	Kolter	slides 11	video 11
2/27	Midterm Exam
3/1	Deep Learning II: Backpropagation	Kolter	slides 12	video 12
3/6	Deep Learning III: Convolutional and Recurrent Models	Kolter	slides 13	video 13
3/8	Probabilistic Modeling I: Probability and maximum likelihood	Kolter	slides 14	video 14
3/13	Spring Break
3/15	Spring Break
3/20	Probabilistic Modeling II: Probabilistic Inference	Kolter	slides 15	video 15
3/22	Probabilistic Modeling III: MCMC	Kolter	slides 16	video 16
3/27	Probabilistic Modeling IV: Bayesian and Deep Generative Models	Kolter	slides 17	video 17
3/29	Game Theory I: Nash Equilibrium	Procaccia	slides 18	video 18
4/3	Game Theory II: Stackelberg and Security	Procaccia	slides 19	video 19
4/5	Game Theory III: Zero Sum and Extensive Form	Procaccia	slides 20	video 20
4/10	Game Theory IV: Minimax Theorem Via No-Regret Learning	Procaccia	slides 21	video 21
4/12	Social Choice I: Voting Rules	Procaccia	slides 22	video 22
4/17	Social Choice II: Manipulation	Procaccia	slides 23	video 23
4/19	Social Choice III: Statistical Approaches	Procaccia	slides 24	video 24
4/24	AI and Education I	Doroudi	slides 25	video 25
4/26	AI and Education II	Doroudi	slides 26	video 26
5/1	AI and Education III	Doroudi	slides 27	video 27
5/3	Q&A	Kolter+Procaccia

Assignments

There will be four assignments (not including HW 0): 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.

Please submit your assignments on Autolab here. Scan handwritten parts of your homework and include them in your autolab submission.

Project

The course project involves carrying out and presenting novel research in AI. Projects are done in groups of 2-4. The expected amount of work is 50 hours per student.

Requirements:

• Proposals (200 words) are due on March 22, and should outline the proposed topic and approach.
• Progress reports (2 pages) are due on April 20.
• Projects will be presented via short videos in the course final exam slot (May 12, 5:30pm-8:30pm). Project reports (up to 5 pages) are due on the same day.

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 5 late days, but cannot use more than 2 late days per homework. No late days may be used for HW 0. No credit will be given for homework submitted more than 2 days after the due date. After your 5 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 discuss both the programming and wirtten protions with other students, but all final submitted work (code and the writeups), but be done entirely on your own. Make sure that you include a README file with your andrew id and your collaborator's andrew id.

Exams

The class includes a midterm exam on Feb 27.

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