Multivariate Gaussian Distribution

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Multivariate Normal Distribution

Covers the multivariate normal distribution, moment-generating function, and combinatorics.

Multivariate Statistics: Normal Distribution

Covers the multivariate normal distribution, properties, and sampling methods.

Linear Combinations: Moment-Generating Functions

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

Linear Models: Least Squares

Explores linear models, least squares, Gaussian vectors, and model selection methods.

Canonical Correlation Analysis: Overview

Covers Canonical Correlation Analysis, a method to find relationships between two sets of variables.

Moment Generating Function and Multivariate Normal Distribution

Explores moment generating functions and multivariate normal distributions in probability and statistics.

Multivariate Statistics: Normal Distribution

Introduces multivariate statistics, covering normal distribution properties and characteristic functions.

Multivariate Statistics: Wishart and Hotelling T²

Explores the Wishart distribution, properties of Wishart matrices, and the Hotelling T² distribution, including the two-sample Hotelling T² statistic.

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 Applications: Vector Spaces and Subspaces

Explores linear applications in vector spaces, emphasizing subspaces and properties of linear maps.