Variational Bayesian methods are a family of techniques used to approximate intractable integrals in Bayesian inference and machine learning. They are used to estimate posterior distributions of parameters and latent variables, and can be seen as an extension of the expectation-maximization algorithm. Compared to Gibbs sampling, variational Bayes is often faster but requires more work to derive the equations used to update the parameters.
Stanford University
Fall 2022
This course emphasizes leveraging shared structures in multiple tasks to enhance learning efficiency in deep learning. It provides a thorough understanding of multi-task and meta-learning algorithms with a focus on topics like self-supervised pre-training, few-shot learning, and lifelong learning. Prerequisites include an introductory machine learning course. The course is designed for graduate-level students.
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+ 17 more conceptsStanford 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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