MCMC methods are a class of algorithms used to sample from a probability distribution. They construct a Markov chain with the desired distribution as its equilibrium, and record states from the chain to obtain a sample. The more steps included, the closer the sample matches the actual desired distribution.

University of Washington

Winter 2022

This course dives deep into the role of probability in the realm of computer science, exploring applications such as algorithms, systems, data analysis, machine learning, and more. Prerequisites include CSE 311, MATH 126, and a grasp of calculus, linear algebra, set theory, and basic proof techniques. Concepts covered range from discrete probability to hypothesis testing and bootstrapping.

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+ 41 more conceptsStanford University

Spring 2022

CS 168 provides a comprehensive introduction to modern algorithm concepts, covering hashing, dimension reduction, programming, gradient descent, and regression. It emphasizes both theoretical understanding and practical application, with each topic complemented by a mini-project. It's suitable for those who have taken CS107 and CS161.

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+ 57 more conceptsUniversity of Washington

Winter 2021

It emphasizes inference in engineering settings, utilizing the powerful language of probabilistic graphical models. This course provides a good blend of probability theory, graph theory, and computation.

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+ 13 more conceptsStanford University

Fall 2022

This course dives into the use of randomness in algorithms and data structures, emphasizing the theoretical foundations of probabilistic analysis. Topics range from tail bounds, Markov chains, to randomized algorithms. The concepts are applied to machine learning, networking, and systems. Prerequisites indicate intermediate-level understanding required.

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