AI Methods for Social Good

17-537 · 17-737 · Spring 2024 · Logistics · Course schedule · Piazza · Canvas

Basic Information

  • Course Name: Artificial Intelligence Methods for Social Good
  • Lectures: Tue/Thu 9:30 am – 10:50 am, Scaife Hall 236 (Please check this Campus Map. It is on CMU campus.)
  • Semester: Spring, Year: 2024
  • Units: 9/12, Section(s): 17-537/17-737

Instructor Information

  • Name: Dr. Fei Fang
  • Contact Info: Email: feifang@cmu.edu
  • Office location: Zoom or in-person (TCS 321)
  • Office hours: Time TBD; Secure one to two consecutive slots in advance via Calendly

If the OH time does not work for you, you can make an appointment for a 30-minute session by emailing Linda Moreci (laf20@cs.cmu.edu) at least 24 hours in advance.

Note: Links to the Zoom meeting and Calendly are available on Canvas.

TA Information

  • Name: Zhicheng Zhang
  • Contact Info: Email: zczhang@cmu.edu
  • Office location: Zoom or in-person (TCS 463)
  • Office hours: Mon/Thu 3:00 pm - 4:00 pm

Course Description

The rapid advance of 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. We will cover a wide range of AI methods, including:

  • Search, Planning, and Optimization: planning and scheduling, convex optimization, mathematical programming
  • Multiagent Systems: computational game theory, mechanism design, human behavior modeling
  • Machine Learning: classification and regression, clustering, probabilistic graphical models, deep learning, reinforcement learning In addition to providing a deep understanding of these methods, the course will introduce which societal challenges they can tackle and how through a series of case studies, in various domains including public health, food and agriculture, security, environmental sustainability, etc. The course will also cover special topics such as the ethics of AI, common challenges in AI for Social Good problems, how to measure the impact of AI for Social Good projects, etc.

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. The students will work in groups on a project exploring the possibility of using AI to help address a societal problem, with a project report and oral presentation delivered at the end of the semester.

Prerequisites:

  • (17537, 9 Units) The students in this 9-unit course are expected to have taken at least three mathematics courses covering linear algebra, calculus, and probability.
  • (17737, 12 Units) This 12-unit course is only open to graduate students (master’s and Ph.D. students) with previous programming experience and background knowledge in artificial intelligence.

Please see the instructor if you are unsure whether your background is suitable for the course.