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Related lectures (30)

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Digital Filtering Fundamentals

Introduces fundamental notions in digital filtering, covering 2D filtering approaches, linear filters, stability, FIR and IIR filters, frequency domain filtering, and Gaussian filters.

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.

Kernel Methods: Machine Learning

Covers Kernel Methods in Machine Learning, focusing on overfitting, model selection, cross-validation, regularization, kernel functions, and SVM.

Image Filtering: Basics and Techniques

Explores image filtering techniques, including linear and nonlinear filters, for artifact removal and feature enhancement.

Neural Networks and Quantum Field Theory

Explores the connection between neural networks and quantum field theory, focusing on the correspondence between parameter and function spaces.

Image Processing Techniques

Covers image processing techniques including noise addition, filtering, and image enhancement using various filters and tools.

Applications of Gauss's law: Electric Field Calculations Simplified

Simplifies electric field calculations using Gauss's law and symmetry arguments.

Robustness and Diffusion Models

Explores robustness in GAN training, Gaussian algorithms, saddle points, and mixed Nash equilibrium.

Gaussian Process-Based Model Predictive Control

Explores Gaussian Process-Based Model Predictive Control for HVAC systems using Gaussian processes, Bayesian statistics, and non-linear equations.