Johns Hopkins University 3 credits Computer Science
Humans, even young children, can learn, model, and reason about the world and other people in a fast, robust, and data efficient way. This course will discuss the principles of human cognition, how we can use machine learning and AI models to computationally capture these principles, and how these principles can help us build better AI. Topics will include (but are not limited to) Bayesian concept learning, probabilistic programming, intuitive physics, decision-making, Theory of Mind, pragmatics, and value alignment. Strongly recommended: a prior course in machine learning or artificial intelligence. Prerequisite(s): Students who have taken EN.601.673 are not eligible to enroll in EN.601.473.;((((EN.553.420 OR EN.553.421) AND (EN.553.430 OR EN.553.431)) OR (EN.553.211 OR EN.553.310 OR EN.553.311) AND (AS.110.201 OR AS.110.212 OR EN.553.291 OR EN.553.295) AND (EN.500.112 OR EN.500.113 OR EN.500.114 OR EN.601.220 OR AS.250.205)))
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