Math 139 Fourier Analysis Lecture 05: Convolutions and Approximation of the Identity
Convolutions and Good Kernels. Definition of convolution. Convolution with the n-th Dirichlet kernel yields the n-th partial sum of the Fourier series. Basic properties of convolution; continuity of the convolution of integrable functions.
From playlist Course 8: Fourier Analysis
Definition of a Discrete Probability Distribution
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Definition of a Discrete Probability Distribution
From playlist Statistics
Probability Distribution Functions and Cumulative Distribution Functions
In this video we discuss the concept of probability distributions. These commonly take one of two forms, either the probability distribution function, f(x), or the cumulative distribution function, F(x). We examine both discrete and continuous versions of both functions and illustrate th
From playlist Probability
(PP 6.1) Multivariate Gaussian - definition
Introduction to the multivariate Gaussian (or multivariate Normal) distribution.
From playlist Probability Theory
Random variables, means, variance and standard deviations | Probability and Statistics
We introduce the idea of a random variable X: a function on a probability space. Associated to such a function is something called a probability distribution, which assigns probabilities, say p_1,p_2,...,p_n to the various possible values of X, say x_1,x_2,...,x_n. The probabilities p_i h
From playlist Probability and Statistics: an introduction
The normal distribution | Probability and Statistics | NJ Wildberger
In this final lecture in this short introduction to Probability and Statistics, we introduce perhaps the most important probability distibution: the normal distribution, also known as the `bell-curve'. Its role is clarified by the Central Limit theorem, a key result in Statistics, that sta
From playlist Probability and Statistics: an introduction
Uniform Probability Distribution Examples
Overview and definition of a uniform probability distribution. Worked examples of how to find probabilities.
From playlist Probability Distributions
Expected Value of a Discrete Probability Distribution
This video explains how to determine the expected value or mean value of a discrete probability distribution. http://mathispower4u.com
From playlist Probability
Ex: Determine Conditional Probability from a Table
This video provides two examples of how to determine conditional probability using information given in a table.
From playlist Probability
Lecture 10 | The Fourier Transforms and its Applications
Lecture by Professor Brad Osgood for the Electrical Engineering course, The Fourier Transforms and its Applications (EE 261). Professor Osgood introduces the final operation of convolution to the central limit theorem. The Fourier transform is a tool for solving physical problems. In t
From playlist Fourier
Why is the most common total of two dice 7? A *Very* Deep Look
Created by Arthur Wesley and Jack Samoncik This video is an informal mathematical proof of the central limit theorem, using the sums of an arbitrary number of dice as an example Music: Chapter 1: https://www.youtube.com/watch?v=eFpJRGB32Ss Chapter 2: https://www.youtube.com/watch?v=g1pS0
From playlist Summer of Math Exposition 2 videos
CS231n Lecture 13 - Segmentation, soft attention, spatial transformers
Segmentation Soft attention models Spatial transformer networks
From playlist CS231N - Convolutional Neural Networks
Integral Transforms Lecture 7: The Fourier Transform. Oxford Mathematics 2nd Year Student Lecture
This short course from Sam Howison, all 9 lectures of which we are making available (this is lecture 7), introduces two vital ideas. First, we look at distributions (or generalised functions) and in particular the mathematical representation of a 'point mass' as the Dirac delta function.
From playlist Oxford Mathematics Student Lectures - Integral Transforms
Lecture 14 | The Fourier Transforms and its Applications
Lecture by Professor Brad Osgood for the Electrical Engineering course, The Fourier Transforms and its Applications (EE 261). Professor Osgood continues to lecture on distributions. The Fourier transform is a tool for solving physical problems. In this course the emphasis is on relatin
From playlist Lecture Collection | The Fourier Transforms and Its Applications
Benson Au: "Finite-rank perturbations of random band matrices via infinitesimal free probability"
Asymptotic Algebraic Combinatorics 2020 "Finite-rank perturbations of random band matrices via infinitesimal free probability" Benson Au - University of California, San Diego (UCSD) Abstract: Free probability provides a unifying framework for studying random multi-matrix models in the la
From playlist Asymptotic Algebraic Combinatorics 2020
Eigenvalue bounds on sums of random matrices - Adam Marcus
Members’ Seminar Topic:Eigenvalue bounds on sums of random matrices Speaker: Adam Marcus Affilation: Princeton University Date: November 14, 2016 For more videos, visit http://video.ias.edu
From playlist Mathematics
A Gentle Introduction to Machine Learning (Lecture 2) by Narayanan Krishnan
PROGRAM TIPPING POINTS IN COMPLEX SYSTEMS (HYBRID) ORGANIZERS: Partha Sharathi Dutta (IIT Ropar, India), Vishwesha Guttal (IISc, India), Mohit Kumar Jolly (IISc, India) and Sudipta Kumar Sinha (IIT Ropar, India) DATE: 19 September 2022 to 30 September 2022 VENUE: Ramanujan Lecture Hall an
From playlist TIPPING POINTS IN COMPLEX SYSTEMS (HYBRID, 2022)
Lecture 24 | The Fourier Transforms and its Applications
Lecture by Professor Brad Osgood for the Electrical Engineering course, The Fourier Transforms and its Applications (EE 261). Professor Osgood continues his lecture on linear systems. The Fourier transform is a tool for solving physical problems. In this course the emphasis is on rela
From playlist Lecture Collection | The Fourier Transforms and Its Applications
CS231n Lecture 14 - Videos and Unsupervised Learning
ConvNets for videos Unsupervised learning
From playlist CS231N - Convolutional Neural Networks
(PP 6.7) Geometric intuition for the multivariate Gaussian (part 2)
How to visualize the effect of the eigenvalues (scaling), eigenvectors (rotation), and mean vector (shift) on the density of a multivariate Gaussian.
From playlist Probability Theory