A Markov chain is a model that describes a sequence of events where the probability of each event depends only on the previous event. It can be used to study various real-world processes and is the basis for stochastic simulation methods. The terms Markovian and Markov are used to describe anything related to a Markov process.
Stanford University
Autumn 2022
The course addresses both classic and recent developments in counting and sampling. It covers counting complexity, exact counting via determinants, sampling via Markov chains, and high-dimensional expanders.
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+ 52 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 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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+ 37 more conceptsBrown University
Spring 2022
This analytical course dives into the mathematical underpinnings of computing successes like machine learning and cryptography, emphasizing the role of probability, randomness, and statistics. Students will explore mathematical models, theorems, and proofs. Practical implementations are not covered, focusing instead on the theories driving computational probabilities.
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