Graduate Artificial Intelligence
This course provides a broad perspective on AI, covering (i) classical approaches of search and planning useful for robotics, (ii) integer programming and continuous optimization that form the bedrock for many AI algorithms, (iii) modern machine learning techniques including deep learning that power many recent AI applications, (iv) game theory and multi-agent systems, and (v) issues of bias and unfairness in AI. In addition to understanding the theoretical foundations, we will also study modern algorithms in the research literature.
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.
|Aditi Raghunathanfirstname.lastname@example.org||Thursdays 1 pm - 2 pm (GHC 7005)|
|Tuomas Sandholmemail@example.com||Mondays 3:20 pm - 4:20 pm (while walking from class to GHC 9205 and then at GHC 9205)|
|Siddharth Prasadfirstname.lastname@example.org||Fridays 4 pm - 5 pm, Gates 5th floor tables by Pausch Bridge|
|Brian Zhangemail@example.com||Tuesdays 3:15 pm - 4:15 pm, Gates 5th floor tables by Pausch Bridge|
|HW||Release date||Due date|
|1 (download link)||1/30||2/13|
|2 (download link)||2/15||3/5|
|3 (download link)||3/1 (release date delayed to 3/5)||3/22|
|Project proposal instructions||3/5||3/17|
|4 (download link)||3/22||4/10|
|5 (download link)||4/12||4/24|
|1/18||Introduction. slides||Raghunathan/Sandholm||Chapters 1 and 2 (Chapter 3 optional)|
|1/23||Search. slides||Sandholm||Sections 3.1 - 3.4|
|1/25||Constraint Satisfaction, SAT. slides||Sandholm||Section 6|
|1/30||Constraint Satisfaction, SAT. slides||Sandholm||HW 1 released|
|2/1||Informed search. slides||Sandholm||Sections 3.5-3.6|
|2/6||Informed search. slides||Sandholm|
|2/8||Linear programming. slides||Raghunathan|
|2/13||Advanced informed search, integer programming. slides||Sandholm||HW 1 due|
|2/15||Advanced informed search, integer programming. slides||Sandholm||HW 2 released|
|2/20||Continuous Optimization - I. slides||Raghunathan|
|2/22||Continuous Optimization - II. slides||Raghunathan|
|2/27||Guest lecture - Binary decision diagrams for discrete optimization. slides||Willem-Jan van Hoeve|
|3/1||Midterm||HW 2 due date extended to 3/5. HW 3 released (3/5).|
|3/13||Machine Learning - I. slides||Raghunathan|
|3/15||Machine Learning - II. slides||Raghunathan||Project proposal due 3/17.|
|3/20||Machine learning - III. slides||Raghunathan|
|3/22||Probabilistic Graphical Models - I. slides 1, slides 2||Raghunathan||HW 3 due/HW 4 released|
|3/27||Probabilistic Graphical Models - II. slides 1, slides 2||Raghunathan|
|3/29||Reinforcement Learning. slides 1, slides 2||Raghunathan|
|4/3||Reinforcement Learning. slides||Raghunathan|
|4/5||Game Theory I - Game Representations, Solution Concepts, and Refinements of Nash Equilibrium. slides||Sandholm|
|4/10||Game Theory I - Game Representations, Solution Concepts, and Refinements of Nash Equilibrium. slides||Sandholm||HW 4 due|
|4/12||Game Theory II - Regret Minimization and Applications to Solving Games. slides||Sandholm||HW 5 Released|
|4/17||Superhuman two-player no-limit Texas hold'em: Libratus. Subgame solving in imperfect-information games. Self-improver. slides||Sandholm||Science 2018 paper|
|4/19||Depth-limited subgame solving. Superhuman multi-player no-limit Texas hold'em. slides||Sandholm||Science 2019 paper|
|4/24||Guest lecture - How AI enables the transition to Driverless Vehicles||Drew Bagnell, Chief Scientist and Co-founder, Aurora Innovation, Consulting Professor, Carnegie Mellon||HW 5 due|
|4/26||Project poster session|
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.
Instructions for submitting homework will be added soon.
You can use no more than 3 late days per assignment and no more than a total of 8 late days over the semester. These late days can and should be used in the event that something comes up that you did not plan for. You do not need to notify the course staff if you plan to use them. No credit will be given for assignments submitted more than 3 days (72 hours) after the posted deadline.
You can discuss both the programming and written portions with other students, but all final submitted work (code and writeups) must be done entirely on your own, without looking at any notes generated during group discussions. Be sure to mention your collaborators' names and Andrew IDs in your writeup.
Can search the internet for references but you are not allowed to post the questions on stackoverflow or anywhere else.
If you reference any code or sources other than the materials provided on the course website or the textbook, you must mention the source. If you have any questions about whether or not you can use a source, please ask.
The course project can be done in groups of 1-3.
If you have a disability and have an accommodations letter from the Disability Resources office, we encourage you to discuss your accommodations and needs with us as early in the semester as possible. We will work with you to ensure that accommodations are provided as appropriate. If you suspect that you may have a disability and would benefit from accommodations but are not yet registered with the Office of Disability Resources, we encourage you to visit their website.
Take care of yourself. Do your best to maintain a healthy lifestyle this semester by eating well, exercising,
getting enough sleep, and taking some time to relax. This will help you achieve your goals and cope with
All of us benefit from support during times of struggle. There are many helpful resources available on campus and an important part of the college experience is learning how to ask for help. Asking for support sooner rather than later is almost always helpful.
If you or anyone you know experiences any academic stress, difficult life events, or feelings like anxiety or depression, we strongly encourage you to seek support. Counseling and Psychological Services (CaPS) is here to help: call 412-268-2922 and visit their website at http://www.cmu.edu/counseling/. Consider reaching out to a friend, faculty or family member you trust for help getting connected to the support that can help.
If you have questions about this or your coursework, please let us know. Thank you, and have a great semester.
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Unfortunately, incidents of bias or discrimination do occur, whether intentional or unintentional. They contribute to creating an unwelcoming environment for individuals and groups at the university. Therefore, the university encourages anyone who experiences or observes unfair or hostile treatment on the basis of identity to speak out for justice and support, within the moment of the incident or after the incident has passed. Anyone can share these experiences using the following resources:
All reports will be documented and deliberated to determine if there should be any following actions. Regardless of incident type, the university will use all shared experiences to transform our campus climate to be more equitable and just.