Kernel Recipes 2022 - Checking your work: validating the kernel by building and testing in CI
The Linux kernel is one of the most complex pieces of software ever written. Being in ring 0, bugs in the kernel are a big problem, so having confidence in the correctness and robustness of the kernel is incredibly important. This is difficult enough for a single version and configuration
From playlist Kernel Recipes 2022
Determine the Kernel of a Linear Transformation Given a Matrix (R3, x to 0)
This video explains how to determine the kernel of a linear transformation.
From playlist Kernel and Image of Linear Transformation
http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Representing multivariate random signals using principal components. Principal component analysis identifies the basis vectors that describe the la
From playlist Random Signal Characterization
Kernel of a group homomorphism
In this video I introduce the definition of a kernel of a group homomorphism. It is simply the set of all elements in a group that map to the identity element in a second group under the homomorphism. The video also contain the proofs to show that the kernel is a normal subgroup.
From playlist Abstract algebra
Introduction to the Kernel and Image of a Linear Transformation
This video introduced the topics of kernel and image of a linear transformation.
From playlist Kernel and Image of Linear Transformation
Support Vector Machines (3): Kernels
The kernel trick in the SVM dual; examples of kernels; kernel form for least-squares regression
From playlist cs273a
The Kernel Trick - THE MATH YOU SHOULD KNOW!
Some parametric methods, like polynomial regression and Support Vector Machines stand out as being very versatile. This is due to a concept called "Kernelization". In this video, we are going to kernelize linear regression. And show how they can be incorporated in other Algorithms to solv
From playlist The Math You Should Know
Determine a Basis for the Kernel of a Matrix Transformation (3 by 4)
This video explains how to determine a basis for the kernel of a matrix transformation.
From playlist Kernel and Image of Linear Transformation
Applied Machine Learning 2019 - Lecture 16 - NMF; Outlier detection
Non-negative Matrix factorization for feature extraction Outlier detection with probabilistic models Isolation forests One-class SVMs Materials and slides on the class website: https://www.cs.columbia.edu/~amueller/comsw4995s19/schedule/
From playlist Applied Machine Learning - Spring 2019
Stanford CS229: Machine Learning | Summer 2019 | Lecture 23 - Course Recap and Wrap Up
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3B6WitS Anand Avati Computer Science, PhD To follow along with the course schedule and syllabus, visit: http://cs229.stanford.edu/syllabus-summer2019.html
From playlist Stanford CS229: Machine Learning Course | Summer 2019 (Anand Avati)
ML Tutorial: Probabilistic Dimensionality Reduction, Part 1/2 (Neil Lawrence)
Machine Learning Tutorial at Imperial College London: Probabilistic Dimensionality Reduction, Part 1/2 Neil Lawrence (University of Sheffield) March 11, 2015
From playlist Machine Learning Tutorials
2020.05.28 Andrew Stuart - Supervised Learning between Function Spaces
Consider separable Banach spaces X and Y, and equip X with a probability measure m. Let F: X \to Y be an unknown operator. Given data pairs {x_j,F(x_j)} with {x_j} drawn i.i.d. from m, the goal of supervised learning is to approximate F. The proposed approach is motivated by the recent su
From playlist One World Probability Seminar
ML Tutorial: Probabilistic Dimensionality Reduction, Part 2/2 (Neil Lawrence)
Machine Learning Tutorial at Imperial College: Probabilistic Dimensionality Reduction, Part 2/2 Neil Lawrence (University of Sheffield) October 21, 2015
From playlist Machine Learning Tutorials
Outliers in weakly Confined Coulomb-type systems by Alon Nishry
PROGRAM: TOPICS IN HIGH DIMENSIONAL PROBABILITY ORGANIZERS: Anirban Basak (ICTS-TIFR, India) and Riddhipratim Basu (ICTS-TIFR, India) DATE & TIME: 02 January 2023 to 13 January 2023 VENUE: Ramanujan Lecture Hall This program will focus on several interconnected themes in modern probab
From playlist TOPICS IN HIGH DIMENSIONAL PROBABILITY
Seventh Imaging & Inverse Problems (IMAGINE) OneWorld SIAM-IS Virtual Seminar Series Talk
Date: Wednesday, December 2, 10:00am EDT Speaker: Martin Burger, FAU Title: Nonlinear spectral decompositions in imaging and inverse problems Abstract: This talk will describe the development of a variational theory generalizing classical spectral decompositions in linear filters and si
From playlist Imaging & Inverse Problems (IMAGINE) OneWorld SIAM-IS Virtual Seminar Series
Mohamed Ndaoud - Constructing the fractional Brownian motion
In this talk, we give a new series expansion to simulate B a fractional Brownian motion based on harmonic analysis of the auto-covariance function. The construction proposed here reveals a link between Karhunen-Loève theorem and harmonic analysis for Gaussian processes with stationarity co
From playlist Les probabilités de demain 2017
Omer Bobrowski: Random Simplicial Complexes, Lecture I
A simplicial complex is a collection of vertices, edges, triangles, tetrahedra and higher dimensional simplexes glued together. In other words, it is a higher-dimensional generalization of a graph. In recent years there has been a growing effort in developing the theory of random simplicia
From playlist Workshop: High dimensional spatial random systems
Elisabeth Gassiat - Manifold Learning with Noisy Data
It is a common idea that high dimensional data (or features) may lie on low dimensional support making learning easier. In this talk, I will present a very general set-up in which it is possible to recover low dimensional non-linear structures with noisy data, the noise being totally unkno
From playlist 8th edition of the Statistics & Computer Science Day for Data Science in Paris-Saclay, 9 March 2023
Concept Check: Describe the Kernel of a Linear Transformation (Reflection Across y-axis)
This video explains how to describe the kernel of a linear transformation that is a reflection across the y-axis.
From playlist Kernel and Image of Linear Transformation