A Bayesian network is a graphical model that represents variables and their dependencies using a directed acyclic graph. It can be used to predict the likelihood of different causes given an event, such as diseases based on symptoms. Efficient algorithms can perform inference and learning in Bayesian networks, and there are also dynamic Bayesian networks for modeling sequences of variables and influence diagrams for solving decision problems under uncertainty.
UC Berkeley
Fall 2022
UC Berkeley's CS 188 course covers the basic ideas and techniques for designing intelligent computer systems, emphasizing statistical and decision-theoretic modeling. By the course's end, students will have built autonomous agents that can make efficient decisions in a variety of settings.
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+ 20 more conceptsCarnegie Mellon University
Spring 2019
This course from Carnegie Mellon University provides a deep understanding of AI's theory and practice, covering methods for decision-making, problem-solving, and handling uncertainty. Topics include search algorithms, computational game theory, and AI ethics.
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+ 24 more concepts