Lectures related to Extreme Value Theory

Covers continuous random variables, probability density functions, and distributions, with practical examples.

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.

Introduction to Continuous Random Variables: Probability Distributions

Introduces continuous random variables and their probability distributions, emphasizing their applications in statistics and data science.

Fundamental Limits of Gradient-Based Learning

Delves into the fundamental limits of gradient-based learning on neural networks, covering topics such as binomial theorem, exponential series, and moment-generating functions.

Kinetic Theory: Distribution Function Evolution

Explores the evolution of the distribution function describing particles in phase space.

Plasma Physics: Self-consistent Description MOOC: Plasma Physics: Introduction

Covers the self-consistent description of a plasma and conservation principles.

Normal Distribution: Properties and Calculations

Covers properties and calculations related to the normal distribution, including probabilities and quantiles.

Probability and Statistics

Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.

Threshold Exceedances: Generalized Pareto Distribution

Explores threshold exceedances and the Generalized Pareto Distribution for modeling extreme data points above a specified level.

Families of Distributions: PDFs and CDFs

Explores PDFs and CDFs for discrete and continuous random variables, including Gaussian, uniform, and exponential distributions.

Continuous Random Variables: Basic Ideas

Explores continuous random variables and their properties, including support and cumulative distribution functions.

Statistical Inference: Random Variables

Covers random variables, probability functions, expectations, variances, and joint distributions.

Fluid Quantities and Two-Fluid Model MOOC: Plasma Physics: Introduction

Covers fluid quantities, moments of kinetic equation, two-fluid model, and plasma turbulence simulation.

Brownian Motion: Fundamentals and Applications

Explores the fundamentals of Brownian motion, including particle positions and distribution functions.

Discrete Random Variables

Covers properties and transformations of discrete random variables, focusing on PMF and expectation.

Collisionless Boltzmann Equation

Explores the Collisionless Boltzmann equation, distribution functions, Jeans theorems, resonances, chaotic orbits, and equilibria in stellar systems.

Random Variables: Expectation and Independence

Explores random variables, expectation, and independence in probability theory.

Probability and Statistics

Covers fundamental concepts in probability and statistics, including distributions, properties, and expectations of random variables.

Probability and Statistics: Discrete Random Variables

Explores discrete random variables, mass functions, and distribution functions in probability and statistics.

Probability Distributions: Central Limit Theorem and Applications Log in to Mediaspace to watch this video

Discusses probability distributions and the Central Limit Theorem, emphasizing their importance in data science and statistical analysis.