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.
Stanford University
Winter 2023
An in-depth study of probabilistic graphical models, combining graph and probability theory. Equips students with the skills to design, implement, and apply these models to solve real-world problems. Discusses Bayesian networks, exact and approximate inference methods, etc.
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+ 14 more conceptsStanford University
Autumn 2022-2023
Stanford's CS 221 course teaches foundational principles and practical implementation of AI systems. It covers machine learning, game playing, constraint satisfaction, graphical models, and logic. A rigorous course requiring solid foundational skills in programming, math, and probability.
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+ 88 more conceptsBrown University
Fall 2022
CS1410 at Brown University delves into the realm of Artificial Intelligence. Using the 3rd edition of "Artificial Intelligence, A Modern Approach" by Russell & Norvig, students explore intelligent agents, game theory, knowledge representation, logic, probabilistic learning, NLP, robotics, computer vision, and ethical implications of AI.
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+ 22 more concepts