Large Deviations: Theory and Applications

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Stochastic Processes: Review and Properties

Covers the review of random variables, probability density functions, variance, and Gaussian processes.

Common Distributions: Moments and MGFs

Covers common distributions, moment generating functions, and covariance matrices in statistics for data science.

Probability and Statistics

Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.

Probability and Statistics: Fundamentals

Covers the fundamental concepts of probability and statistics, including interesting results, standard model, image processing, probability spaces, and statistical testing.

Central Limit Theorem

Covers the Central Limit Theorem and its application to random variables, proving convergence to a normal distribution.

Elements of Statistics: Estimation & Distributions

Covers fundamental statistics concepts, including estimation theory, distributions, and the law of large numbers, with practical examples.

Probability Distributions: Basics and Properties

Covers the basics of probability distributions, including mean, variance, and properties of random variables.

Probability Theory: Random Variables and Distributions

Introduces probability theory, random variables, and distributions, with a focus on their applications in atomic diffusion.

Probability Distributions: Independence and Linear Combinations

Explores independence, linear combinations of variables, and probability laws.

Random Variables and Expected Value

Introduces random variables, probability distributions, and expected values through practical examples.