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Related lectures (30)

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Brownian Motion: Theory and Applications

Covers the theory of Brownian motion, diffusion, and random walks, with a focus on Einstein's theory for one-dimensional motion.

Branch and Bound: Heuristic Maximization

Explains the Branch and Bound algorithm for heuristic maximization problems using LP relaxations and pruning techniques.

Elements of Statistics: Probability and Random Variables

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Probability and Statistics: Fundamental Theorems

Explores fundamental theorems in probability and statistics, joint probability laws, and marginal distributions.

Parallel Scan Left

Introduces parallel scan left in Scala, covering its properties, sequential solutions, and efficient parallel computation techniques.

Advanced Probability: Bayes' Theorem and Random Variables

Covers advanced probability concepts, including Bayes' Theorem and Random Variables.

Luria-Delbrück Experiment

Explores the implications of the Luria-Delbrück experiment on evolutionary mechanisms and the importance of probabilities in understanding biological data.

Building a Decision Tree

Covers building decision trees to classify mushrooms as poisonous or not.

Stochastic Models for Communications

Covers stochastic models for communications, focusing on random variables, Markov chains, Poisson processes, and probability calculations.