For students on the waitlist: some course materials in the first few weeks are available on this webpage. Check the references in the course schedule section below.
Please fill the auditing form. Please note that the classroom has limited seats and priority will be given to the students who registered for the class.
Some lectures will be recorded and can be accessed through Panopto with an Andrew ID [link].
Syllabus [pdf version]
|Artificial Intelligence Methods for Social Good|
|Meeting Days, Times, Location: Tue/Thu 10:30am-11:50am, GHC 5222|
|Semester: Spring, Year: 2018|
|Units: 9/12, Section(s): 08-537/08-737|
|Name||Dr. Fei Fang|
|Contact Info||Email: firstname.lastname@example.org|
|Office location||Wean Hall 4126|
|Office hours||Tue/Thu 1pm-2pm|
|TA name||Chun Kai Ling|
|TA Contact Info||Email: email@example.com|
|Office location||GHC 6507|
The rapid advance in artificial intelligence (AI) has opened up new possibilities of using AI to tackle the most challenging societal problems today. This course brings together a set of advanced AI methods that allow us to address such challenges and promote social good:
- Optimization: mathematical programming, robust optimization, influence maximization
- Game Theory and Mechanism Design: security games, human behavior modeling, auction and market equilibrium, citizen science
- Machine Learning: classification, clustering, probabilistic graphical models, deep learning
- Sequential Decision Making: Markov Decision Processes (MDPs), partially observable MDPs, online planning, reinforcement learning
In addition to providing a deep understanding of these methods, the course will introduce which societal challenges they can tackle and how, in the areas of (i) healthcare, (ii) social welfare, (iii) security and privacy, (iv) environmental sustainability. The course will also cover special topics such as AI and Ethics and AI and Humans. Example research projects and social good outcomes can be found at http://aiandsocialgood.org
The course content is designed to not have too much overlap with other AI courses offered at CMU. Although the course is listed within SCS, it should be of interest to students in several other departments, including ECE, EPP and SDS.
(9 Unit) The students in this 9-unit course are expected to have taken at least three mathematics courses covering linear algebra, calculus, and probability. The students will work in groups on a systematic literature review or a project exploring the possibility of applying existing AI tools to a societal problem, with a survey paper or technical report and presentation delivered at the end of the semester.
(12 Unit) This 12-unit course is only open to graduate students (master and Ph.D. students) with previous programming experience and background knowledge in artificial intelligence. The students will work in groups on a research project with a research-style paper and an oral presentation delivered at the end of the semester. Please see the instructor if you are unsure whether your background is suitable for the course.
At the end of the course, the students should be able to
- Identify societal challenges that can potentially be tackled by AI methods, and determine which AI methods can be applied
- Describe the AI methods covered in the course, including the basic concepts, the key algorithms, and the commonly-used implementation of the methods
- Model the societal challenges as mathematical problems that AI techniques can be applied and propose how to adjust and modify the AI techniques to fit the problems
- Describe evaluation criteria and methodologies of applying AI methods for social good
- Deliver written and oral presentation on research projects or research survey
No formal textbook. References and additional resources will be provided in slides and on Canvas.
The final course grade will be calculated using the following categories:
|Assessment||Percentage of Final Grade|
|Class participation||10 points|
|Paper Summaries||20 points|
|Written Answers Assignment||20 points|
|Final Project||50 points|
- Class participation. The grading of the class participation will be mostly based on attendance, checked by in-class quizzes and asking and answering questions in class. Other factors include asking and answering questions on Canvas.
- Paper reading assignment. The course will require all students to complete weekly paper reading assignments individually. In each assignment, the students are required to provide a summary of the paper/article, a list of questions, and a few brainstorming ideas. The assignments will be submitted through Canvas and will be peer reviewed, but the final score will be provided by the instructor and the TA. For each student, the lowest scored assignment will be dropped.
- Written answers assignment. The course will require all students to complete biweekly written answers assignment individually. Each written answers assignment will involve checking the understanding of basic concepts and working through the algorithms presented in class on example problems. Most questions are multiple-choice questions or numerical answer questions. In addition to providing the answers, the students are required to provide explanations to the answers. The answers will be submitted and auto-graded through Canvas. The explanations will be checked through peer review. For each student, the lowest scored assignment will be dropped.
- Final project. The students will work in small groups (1-3 students in each group). The students are expected to focus on one or more societal challenges, summarize or propose models and AI-based solutions to tackle the challenges, and evaluate the solutions. The students are required to submit project report through Canvas and deliver oral presentation. The instructor will provide suggested project topics. The students can also propose their own projects topics related to AI and Social Good but they will need consent from the instructor. The progress of projects will be checked through Project Proposal, Project Progress Report, Project Presentation, and Final Project Report. The proposal and progress report will be peer reviewed. The presentation and the final report will be evaluated by instructor and TA directly.
- The students who choose the 12-unit section will work on a research project, with a research-style paper and an oral presentation delivered at the end of the semester. Ph.D. students may choose a paper format with the guidance of their advisors. For master students, a sample research-style paper can be found here: https://feifanginfo.files.wordpress.com/2016/11/2015_aaai_csworkshop_repeatedssg.pdf
- The students who choose the 9-unit section will work on a systematic literature review or a project exploring the possibility of applying existing AI tools to a social good problem, with a survey paper or technical report and presentation delivered at the end of the semester.
- Sample survey paper: http://www.sigecom.org/exchanges/volume_15/1/FANG.pdf
- Sample technical report: https://www.aaai.org/ocs/index.php/SSS/SSS12/paper/view/4310/4644
Students will be assigned the following final letter grades, based on calculations coming from the course assessment section.
|R (F)||[0,59) points|
- Late-work policy and Make-up work policy: No late days or make-up work is allowed. However, for both paper reading assignments and written answer assignments, the lowest scored assignment in each category will be dropped.
- Re-grade policy: To request a re-grade, the student need to write an email to the instructor titled “Re-grade request from [Student’s Full Name]” within one week of receiving the graded assignment.
- Attendance and participation policy: Attendance and participation will be a graded component of the course. The grading of the class participation will be mostly based on attendance, checked by in-class quizzes and asking and answering questions in class. Other factors include asking and answering questions on Canvas.
- Academic Integrity & Collaboration: For both paper reading assignments and written answer assignments, a student can discuss with other students, but he need to specify the names of the students he discussed with in the submission, and complete the calculations and writing of explanations, summary, and questions on his own. For the final project, the students can discuss and collaborate with others (including students, faculty members, and domain experts), but the students need to give proper credits to whoever involved, and report the contributions of each group member in the final report and presentations, which will be considered in the grading. For assignments and final project, it is allowed to use publicly available code packages but the source of code package needs to be specified in the submission. Plagiarism is not allowed. The policy is motivated by CMU policy on academic integrity https://www.cmu.edu/student-affairs/ocsi/academic-integrity/index.html
- Mobile Devices: Mobile devices are allowed in class. Cellphones should be in silent mode. Students who use tablet in upright position and laptops will be asked to sit in the back rows of the classroom.
- Accommodations for students with disabilities: If you have a disability and require accommodations, please contact Catherine Getchell, Director of Disability Resources, 412-268-6121, firstname.lastname@example.org. If you have an accommodations letter from the Disability Resources office, I encourage you to discuss your accommodations and needs with me as early in the semester as possible. I will work with you to ensure that accommodations are provided as appropriate.
- Statement on student wellness: As a student, you may experience a range of challenges that can interfere with learning, such as strained relationships, increased anxiety, substance use, feeling down, difficulty concentrating and/or lack of motivation. These mental health concerns or stressful events may diminish your academic performance and/or reduce your ability to participate in daily activities. CMU services are available, and treatment does work. You can learn more about confidential mental health services available on campus at: http://www.cmu.edu/counseling/. Support is always available (24/7) from Counseling and Psychological Services: 412-268-2922.
Course Schedule (Subject to Change)
|1/16||M0: Introduction, Logistics, Course Project||M0 [pdf] HW0 [pdf]|
|1/18||M1-1 [Optimization]: Optimization Problems
Cover: Convex optimization, Linear Programming (LP) and Mixed Integer Linear Programming (MILP)
|Convex Optimization, Chapters 1-4
Stephen Boyd and Lieven Vandenberghe (Cambridge University Press)
Applied Mathematical Programming, Chapters 2, 9
Bradley, Hax, and Magnanti (Addison-Wesley, 1977)
|1/23||M1-2 [Optimization]: Conservation Planning
Cover: Wildlife corridor design
|PRA1 due||(PRA1) Solving Connected Subgraph Problems in Wildlife Conservation
Bistra Dilkina & Carla P. Gomes
Ronan Le Bras, Bistra Dilkina, Yexiang Xue, Carla P. Gomes, Kevin S. McKelvey, Michael K. Schwartz, Claire A. Montgomery
|1/25||M2-1 [Game Theory]: Basics of Game Theory
Cover: Minimax theory, Nash Equilibrium, Stackelberg Equilibrium
|HW0 due||Algorithmic Game Theory, Chapters 1-3
Editors: Noam Nisan, Tim Roughgarden, Eva Tardos, Vijay V. Vazirani (Cambridge University Press)
|1/30||M2-2 [Game Theory]: Security Games
Cover: Ferry protection, ranger patrol planning
|(PRA2) Deployed ARMOR Protection: The Application of a Game Theoretic Model for Security at the Los Angeles International Airport
James Pita, Manish Jain, Janusz Marecki, Fernando Ordóñez, Christopher Portway, Milind Tambe, Craig Western, Praveen Paruchuri, Sarit Kraus
Fei Fang, Albert Xin Jiang, Milind Tambe
Fei Fang, Thanh H. Nguyen, Rob Pickles, Wai Y. Lam, Gopalasamy R. Clements, Bo An, Amandeep Singh, Milind Tambe, Andrew Lemieux
|2/1||M2-3 [Game Theory]: Human Behavior Modeling
Cover: Prospect theory, quantal response, cognitive hierarchy
Submit Final Project Group Member List
| (PRA4) Comparing Human Behavior Models in Repeated Stackelberg Security Games: An Extended Study
Debarun Kar, Fei Fang, Francesco M. Delle Fave, Nicole Sintov, Milind Tambe, Arnaud Lyet
Rong Yang, Christopher Kiekintveld, Fernando Ordonez, Milind Tambe, Richard John
James R. Wright, Kevin Leyton-Brown
|2/6||Guest Lecture by Prof. Illah Nourbakhsh (Carnegie Mellon University)
AI and Ethics
(may move to other dates in Feb)
|PRA3 due||(PRA3) The Rhetoric of Robotics
Illah Reza Nourbakhsh
|2/8||M3-1 [Machine Learning]: Classification and Clustering
Cover: Decision trees, k-means, Gaussian Mixture Models (GMMs)
|PRA4 due||Pattern Recognition and Machine Learning, Chapters 4, 9
|2/13||M3-2 [Machine Learning]:
Probabilistic Graphical Models
Cover: Dynamic Bayesian Networks (DBNs), Markov Random Fields (MRFs)
|HW2 due||Pattern Recognition and Machine Learning, Chapter 8
|2/15||M3-3 [Machine Learning]: Predicting Illegal Activities
Cover: Predict poaching threat, predict urban crime
|Project Proposal due||(PRA5) Keeping Pace with Criminals: An Extended Study of Designing Patrol Allocation against Adaptive Opportunistic Criminals
Chao Zhang, Shahrzad Gholami, Debarun Kar, Arunesh Sinha, Manish Jain, Ripple Goyal, Milind Tambe
Shahrzad Gholami, Benjamin Ford, Fei Fang, Andrew Plumptre, Milind Tambe, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba, Mustapha Nsubaga, Joshua Mabonga
Debarun Kar, Benjamin Ford, Shahrzad Gholami, Fei Fang, Andrew Plumptre, Milind Tambe, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba
|2/20||M3-4 [Machine Learning]: Regression
Cover: Linear Regression, Regularization, Support Vector Machines (SVMs)
|PRA5 due||Pattern Recognition and Machine Learning, Chapters 3, 6
|2/22||M4-1 [Sequential Decision Making] Markov Decision Process (MDP)
Cover: Value iteration
|HW3 due||Reinforcement Learning: An Introduction, Chapter 3
Richard S. Sutton and Andrew G. Barto
|2/27||M1-3 [Optimization] Combinatorial Optimization and Robust Optimization
Cover: Duality, branch and price, maximin model, minimax regret, prevent illegal fishing
|PRA6 due||Combinatorial Optimization: Algorithms and Complexity, Chapters 3
Christos H. Papadimitriou, Kenneth Steiglitz
Cynthia Barnhart, Ellis L. Johnson, George L. Nemhauser, Martin W. P. Savelsbergh, Pamela H. Vance
William Haskell, Debarun Kar, Fei Fang, Milind Tambe, Sam Cheung, Elizabeth Denicola
|3/1||Guest Lecture by Prof. Tuomas Sandholm (Carnegie Mellon University)
Cover: Kidney exchange
|HW4 due||(PRA6) FutureMatch: Combining Human Value Judgments and Machine Learning to Match in Dynamic Environments.
Dickerson, J. and Sandholm, T.
In Proceedings of the AAAI Conference on Artificial Intelligence. Extended version with appendix.
Dickerson, J., Manlove, D., Plaut, B., Sandholm, T., and Trimble J.
In Proceedings of the ACM Conference on Economics and Computation (EC). Extended version.
|3/6||M4-2 [Sequential Decision Making]: Partially Observable MDPs
Cover: Online Planning, Monte-Carlo Tree Search (MCTS)
|PRA7 due||Monte-Carlo Planning in Large POMDPs
David Silver, Joel Veness
Levente Kocsis and Csaba Szepesvari
|3/8||Guest Lecture by Prof. Norman Sadeh (Carnegie Mellon University)
Cover: Security and privacy, Livehoods: understand city using social media
|(PRA7) The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City
Justin Cranshaw Raz Schwartz Jason I. Hong Norman Sadeh
|3/13||Spring Break, no class|
|3/15||Spring Break, no class|
|3/20||Guest Lecture by Prof. Phebe Vayanos (University of Southern California)
Cover: Kidneys for translation, housing for homeless youth
Project Progress Report due
|Robust Multiclass Queuing Theory for Wait Time Estimation in Resource Allocation Systems
Chaithanya Bandi, Nikolaos Trichakis, Phebe Vayanos
|3/22||Guest Lecture by Prof. Stephen Smith (Carnegie Mellon University)
Cover: Smart traffic light control
|HW5 due||(PRA8) Accomodating High Value-of-Time Drives in Market-Driven Traffic Signal Control
Isaac Isukapati and Stephen Smith
|3/27||M4-3 [Sequential Decision Making]: Invasive Species Management
Cover: Reinforcement Learning, Invasive species management
|PRA9 due||(PRA9) PAC Optimal MDP Planning with Application to Invasive Species Management
Majid Alkaee Taleghan, Thomas G. Dietterich, Mark Crowley, Kim Hall, H. Jo Albers
Thomas G. Dietterich, Majid Alkaee Taleghan, Mark Crowley
|3/29||M1-4 [Optimization]: Influence Maximization
Cover: Influence propagation models, submodular function optimization
|Maximizing the spread of influence through a social network
David Kempe, Jon Kleinberg, Éva Tardos
Wei Chen, Laks V.S. Lakshmanan, Carlos Castillo
|4/3||Guest Lecture by Prof. Milind Tambe (University of Southern California)
Cover: AI and Social Work; HIV prevention among homeless youth
|PRA10 due||(PRA10) Using Social Networks to Aid Homeless Shelters: Dynamic Influence Maximization under Uncertainty
Amulya Yadav, Hau Chan, Albert Jiang, Haifeng Xu, Eric Rice, Milind Tambe
Amulya Yadav, Bryan Wilder, Eric Rice, Robin Petering, Jaih Craddock, Amanda Yoshioka-Maxwell, Mary Hemler, Laura Onasch-Vera, Milind Tambe, Darlene Woo
Bryan Wilder, Amulya Yadav, Nicole Immorlica, Eric Rice, Milind Tambe
|4/5||M3-5 [Machine Learning]: Deep Learning
Cover: Neural Networks (NNs), Convolutional NN, Faster RCNN, Detecting human and wildlife from UAV videos
|HW6 due||Deep Learning, Chapter 6, 9
Ian Goodfellow and Yoshua Bengio and Aaron Courville
Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun
Elizabeth Bondi, Fei Fang, Mark Hamilton, Debarun Kar, Donnabell Dmello, Jongmoo Choi, Robert Hannaford, Arvind Iyer, Lucas Joppa, Milind Tambe, Ram Nevatia
|4/10||Guest Lecture by Prof. Stefano Ermon (Stanford University)
Cover: Deep learning for developing countries
|PRA11 due||(PRA11) Combining satellite imagery and machine learning to predict poverty
Neal Jean, Marshall Burke, Michael Xie, Matthew Davis, David B. Lobell, Stefano Ermon
Jiaxuan You, Xiaocheng Li, Melvin Low, David Lobell, Stefano Ermon
|4/12||M2-4 [Game Theory]: Mechanism Design with Money
Cover: Auction, Truthfulness, Price-of-Anarchy
|Algorithmic Game Theory, Chapters 9, 11
Editors: Noam Nisan, Tim Roughgarden, Eva Tardos, Vijay V. Vazirani (Cambridge University Press)
|4/17||M2-5 [Game Theory]: Spatio-Temporal Pricing in Ridesharing Platforms
Cover: scheduling and pricing, market equilibrium
|HW7 due||(PRA12) Spatio-Temporal Pricing for Ridesharing Platforms
Hongyao Ma, Fei Fang, David C. Parkes
|4/19||CMU’s Carnival, No class||PRA12 due|
|4/24||Guest Lecture by Prof. David Danks (Carnegie Mellon University)
Cover: AI and Humans
|PRA13 due||(PRA13-a) Regulating Autonomous Systems: Beyond Standards
David Danks and Alex John London
|4/26||M2-6 [Game Theory]: Citizen Science
Cover: Citizen science
|(PRA14) Avicaching: A Two Stage Game for Bias Reduction in Citizen Science
Yexiang Xue, Ian Davies, Daniel Fink, Christopher Wood, Carla P. Gomes
Yexiang Xue, Ian Davies, Daniel Fink, Christopher Wood, Carla P. Gomes
Yexiang Xue, Bistra Dilkina, Theodoros Damoulas, Daniel Fink, Carla P. Gomes and Steve Kelling
|5/1||Course Project Presentation 1||PRA14 due|
|5/3||Course Project Presentation 2||HW8 due|
|5/10||Final Project Report due|