08-737 Artificial Intelligence Methods for Social Good
12 units, Carnegie Mellon University
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 societal challenges and promote social good 1 Machine Learning supervised learning, deep learning 2 Game Theory and Mechanism Design security games, human behavior modeling, scheduling and pricing, citizen science 3 Sequential Decision Making Markov Decision Processes MDPs, partially observable MDPs 4 Planning and Optimization influence maximization, online planning, combinatorial optimization 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 ihealthcare, iisocial welfare, iiisecurity and privacy, ivenvironmental sustainability. The course will also cover special topics such as AI and Ethics and Safety of AI. Example research projects and social good outcomes can be found at httpaiandsocialgood.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. This 12 unit course is only open to graduate students masters and PhD with previous programming experience and background knowledge in artificial intelligence. The students will work in groups on a research project with a researchstyle paper and on oral presentation delivered at the en of the semester. Please see the instructor if you are unsure of whether your background is suitable for the course.
Prerequisites: none listed
Courseweave needs JavaScript to show sections, workloads and planning tools.