Lectures related to Advanced Probabilities: Random Variables & Expected Values

Explores different modes of convergence for random variables.

Law of Large Numbers: Strong Convergence

Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.

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.

Linear Combinations: Moment-Generating Functions

Explores moment-generating functions, linear combinations, and normality of random variables.

Convergence of Random Variables

Explores the convergence of random variables, the law of large numbers, and the distribution of failure time.

Variance and Covariance: Properties and Examples

Explores variance, covariance, and practical applications in statistics and probability.

Normal Distribution: Properties and Calculations

Covers the normal distribution, including its properties and calculations.

Conditional Density and Expectation

Explores conditional density, expectations, and independence of random variables with practical examples.