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Related lectures (30)
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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.
Image Processing: Neighborhood Averaging & Gaussian Smoothing
Explores neighborhood averaging, Gaussian smoothing, median filtering, contrast enhancement, and edge detection in image processing.
Image Processing I: Filters and Transformations
Explores moving average and exponential filters, Gaussian filtering, and linear scale-space concepts in image processing.
Image Processing: Smoothing and Filtering
Covers image smoothing, noise reduction, and segmentation techniques using filters and transforms.
Edge and Contour
Covers edge and contour detection in images, including gradient-based methods, Laplacian operator, and more complex methods.
Fourier Transform: Properties and Applications
Covers the properties and applications of the Fourier transform and its relation to Heisenberg's uncertainty principle.
Image Filtering: Classic Filters
Explores classic image filters like Moving Average, Gaussian Blur, Sobel filter, and Laplacian operator.
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.
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.
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