This course is targeted at graduate students who are interested in learning about intelligent agents capable of automating the processes of perceiving and representing the state of the world, making decisions and learning, and coordinating and competing with each other.
Techniques from Probability, Statistics, Economics, Algorithms, Operations Research and Optimal Control are increasingly important tools for improving the intelligence and autonomy of machines, whether those machines are robots traversing our environments, schedulers moving inventory, spacecraft deciding which experiments to perform, or agents finding matches of kidney donors to patients. The course will cover the ideas underlying these techniques.
The course is taught by professors Emma Brunskill (CSD) and Manuela Veloso (CSD). The teaching assistants are Shayan Doroudi (CSD) and Vittorio Perera (CSD). The course is open to graduate students in the School of Computer Science; interested and qualified undergraduates and other students should contact the professors for permission to join.
We meet most Mondays and Wednesdays from 12:00pm to 1:20pm in GHC 4303. The first lecture will be held on Monday, January 12. Check the lecture schedule below for details!
Weekly office hours will be held at the following times and locations:
|Emma Brunskill||By appointment||By appointment||GHC 7217|
|Shayan Doroudi||Tuesday||4-5pm||GHC 8127|
|Vittorio Perera||Monday||3-4pm||GHC 7004|
|Manuela Veloso||By appointment||By appointment||GHC 7002|
All lecture and homework dates and topics are subject to change. This is a rough outline of the topics we will be covering this semester.
Homeworks are due at the begining of class, unless otherwise specified. You will be allowed 8 total late days without penalty for the entire semester. You may use a maximum of 3 late days per individual homework assignment. Each late day corresponds to 24 hours or part thereof. Once those days are used, you will not receive any credit for late homework. You must turn in all of the homeworks, even if for zero credit, in order to pass the course.
|#1<||1/22/2015||2/4/2015>||Search, Heuristics, A*, Optimization, KR, CSP||—||(pdf)|
|#2||2/12/2015||2/25/2015||More CSP, Planning, RL||—||(pdf)|
|#3||3/23/2015||4/6/2015||RMAX, Value of Information, Bayes Nets, HMMs, POMDPs||—||(pdf)|
|#4A||4/11/2015||4/27/2015||Decision Trees, VC-Dimension, kNN||—||(pdf)|
Please choose one of the six topics listed here and send all four course staff an email by 11:59 PM Wednesday, Feb 18 with your choice as well as a list of all of your group members. You should form groups of two or three. If you need to find group members, you can do so here. If necessary, you may form a group of four or work by yourself, but please email us ahead of time if you plan on doing this with your reasoning. April 29: Poster presentations (during class period) May 4: Project reports due. 6 pages max, minimum 3. It can be in a conference layout or a layout of your chosing. It should include a problem statement, approach and results
Grades are based on Homeworks (40%), Final Project (20%), Midterm (20%) and Final (20%).
Interested students should first register, then fill out an audit form and have one of the instructors sign it. Auditors are required to complete a class project, but no homeworks or exams: that way they can choose to focus their efforts on whichever area of AI most interests them.
Feel free to use the slides and materials available online here! If you use our slides, an appropriate attribution is requested. Please email the instructors with any corrections or improvements.