17-737 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 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. 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 12unit 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 researchstyle 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.
Prerequisites: none listed
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