Concept

Gaussian function

Related lectures (32)

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

Provable and Generalizable Robustness in Deep Learning

Explores adversarial examples, defenses, and certifiable robustness in deep learning, including Gaussian smoothing and perceptual attacks.

Statistical Inference and Machine Learning

Covers statistical inference, machine learning, SVMs for spam classification, email preprocessing, and feature extraction.

Understanding Chaos in Quantum Field Theories

Explores chaos in quantum field theories, focusing on conformal symmetry, OPE coefficients, and random matrix universality.

Fourier Transform: Properties and Applications

Covers the properties and applications of the Fourier transform and its relation to Heisenberg's uncertainty principle.

Renormalization Group in Field Theory

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

MLE for Gaussian: EMV in Gaussian Model

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

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.

Gaussian and Polar Representations

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

Image Processing: Neighborhood Averaging & Gaussian Smoothing

Explores neighborhood averaging, Gaussian smoothing, median filtering, contrast enhancement, and edge detection in image processing.

Image Processing: Smoothing and Filtering

Covers image smoothing, noise reduction, and segmentation techniques using filters and transforms.

The Spike-Wigner model

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

Wireless Receivers: Gaussian Noise and Error Probability Estimation

Covers wireless receivers in the presence of Gaussian noise and error probability estimation.

Spectral Estimation: Gaussian vs Binary Signals

Explores spectral estimation for Gaussian and binary signals in the spiked matrix estimation problem, analyzing the impact of signal-to-noise ratio.

Image Filtering: Classic Filters

Explores classic image filters like Moving Average, Gaussian Blur, Sobel filter, and Laplacian operator.

Image Processing I: Filters and Transformations

Explores moving average and exponential filters, Gaussian filtering, and linear scale-space concepts in image processing.

Random Walk: Hitting Probabilities

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

Conditional Gaussian Generation

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

Bayesian Estimation

Covers the fundamentals of Bayesian estimation, focusing on the application of Bayes' Theorem in scalar estimation.

Kernel Methods: Nonlinear Separation Surfaces

Explores kernel methods for nonlinear separation surfaces using polynomial and Gaussian kernels in Perceptron and SVM algorithms.