Fourier Transformation: Fundamentals

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Exponential Family: Distributions and Regularity

Covers the exponential family of distributions and the Ising model, explaining magnetism through dipoles and spins.

Fourier Transform: Basics and Examples

Explains the basics of Fourier transform and demonstrates its application through examples, including periodic functions and Fourier Transform Pairs.

Fourier Analysis: Stokes Theorem

Explores the application of Stokes' theorem to surfaces and vector spaces in Fourier analysis.

Signals & Systems I: Distributions and Linear Systems

Covers distributions, linear systems, impulse response, convolution, and examples of linear systems.

Probability and Statistics

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

Fourier Transform: Concepts and Applications

Covers the Fourier transform, its properties, applications in signal processing, and differential equations, emphasizing the concept of derivatives becoming multiplications in the frequency domain.

Probability and Statistics: Variance and Covariance Calculations

Discusses variance and covariance calculations, transformations of random variables, and mixtures in probability and statistics.

Elements of Statistics: Probability, Distributions, and Estimation

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

Common Distributions: Moment Generating Functions

Explores common probability distributions, special distributions, and entropy concepts.

Fourier Series: Understanding Coefficients and Periodicity

Explores Fourier series coefficients, periodicity, and series relations.