Martingales are used in probability theory to model random processes. They are sequences of random variables where the expected value of the next variable is equal to the current value, regardless of past values. This allows for predicting future values based on present ones.

Stanford 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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