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Related lectures (30)

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Learning the Kernel: Convex Optimization

Explores learning the kernel function in convex optimization, focusing on predicting outputs using a linear classifier and selecting optimal kernel functions through cross-validation.

Random Walk: Hitting Probabilities

Explores random walks, hitting probabilities, recursive structures, and Gaussian processes.

Linear Phase Filters: Step Response and Characteristics

Explores linear phase filters, rise time, Gaussian filters, and rational transfer functions.

Edge Detection: Basics and Techniques

Introduces the basics of edge detection, including measuring contrast, gradient images, Fourier interpretation, Gaussian functions, Canny edge detector, and industrial applications.

Renormalization Group in Field Theory

Explores the Renormalization Group in field theory, discussing scaling functions, critical exponents, and Gaussian fixed points.

Signals & Systems I: Uncertainty Relations and Gaussian Impulse

Explores uncertainty relations, Gaussian impulse, pseudo-probability density, and Gabor functions in signals and systems.

The Spike-Wigner model

Explores the Spike-Wigner model, low-rank matrix factorization, and Gaussian matrices.

Gaussian and Polar Representations

Covers the concepts of complex numbers, orientation, and transformations.

MLE for Gaussian: EMV in Gaussian Model

Discusses Maximum Likelihood Estimation for Gaussian mean and variance, exploring parameter estimation in a Gaussian distribution.

Conditional Gaussian Generation

Explores the generation of multivariate Gaussian distributions and the challenges of factorizing covariance matrices.