The method of iteratively reweighted least squares (IRLS) is used to solve certain optimization problems with objective functions of the form of a p-norm: by an iterative method in which each step involves solving a weighted least squares problem of the form: IRLS is used to find the maximum likelihood estimates of a generalized linear model, and in robust regression to find an M-estimator, as a way of mitigating the influence of outliers in an otherwise normally-distributed data set. For example, by minimizing the least absolute errors rather than the least square errors. One of the advantages of IRLS over linear programming and convex programming is that it can be used with Gauss–Newton and Levenberg–Marquardt numerical algorithms. (Wikipedia).
Comparing Iterative and Recursive Factorial Functions
Comparing iterative and recursive factorial functions
From playlist Computer Science
The hidden beauty of the A* algorithm
00:00 Intro 01:38 Change the lengths! 06:34 What is a good potential? 12:31 Implementation 16:20 Bonus Video of Tom Sláma: https://youtu.be/umszOeerdsU Some related stuff: -- One thing I did not mention is that Dijkstra's algorithm is designed to solve the problem of finding the shortes
From playlist Fourier
Lecture 3 – Word Vectors 2 | Stanford CS224U: Natural Language Understanding | Spring 2019
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Professor Christopher Potts & Consulting Assistant Professor Bill MacCartney, Stanford University http://onlinehub.stanford.edu/ Professor Christopher Potts Pr
From playlist Stanford CS224U: Natural Language Understanding | Spring 2019
Lecture 4 – Word Vectors 3 | Stanford CS224U: Natural Language Understanding | Spring 2019
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Professor Christopher Potts Professor of Linguistics and, by courtesy, Computer Science Director, Stanford Center for the Study of Language and Information http:
From playlist Stanford CS224U: Natural Language Understanding | Spring 2019
MIT 6.006 Introduction to Algorithms, Spring 2020 Instructor: Jason Ku View the complete course: https://ocw.mit.edu/6-006S20 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63EdVPNLG3ToM6LaEUuStEY This lecture focuses on solving any all-pairs shortest paths (APSP) in w
From playlist MIT 6.006 Introduction to Algorithms, Spring 2020
From playlist Plenary talks One World Symposium 2020
TMCF workshop - Theory and methods challenges in counterfactual prediction, Karla Diaz-Ordaz
Prediction algorithms in AI use machine learning and statistics to make predictions about an event, given what we know now. Examples include whether a covid-19 patient will require ventilation, or whether a person seeking insurance will make a claim. These predictions can be used for plann
From playlist Theory and Methods Challenge Fortnights
EXTRA MATH 11B: Finding the least squares estimates
Forelæsning med Per B. Brockhoff. Kapitler:
From playlist DTU: Introduction to Statistics | CosmoLearning.org
Sylvia Biscoveanu - Power Spectral Density Uncertainty and Gravitational-Wave Parameter Estimation
Recorded 19 November 2021. Sylvia Biscoveanu of the Massachusetts Institute of Technology presents "The Effect of Power Spectral Density Uncertainty on Gravitational-Wave Parameter Estimation" at IPAM's Workshop III: Source inference and parameter estimation in Gravitational Wave Astronomy
From playlist Workshop: Source inference and parameter estimation in Gravitational Wave Astronomy
Ik Siong Heng - Gaussian Mixture Models for transient gravitational wave detection - IPAM at UCLA
Recorded 29 November 2021. Ik Siong Heng of the University of Glasgow prsents "Gaussian Mixture Models for transient gravitational wave detection" at IPAM's Workshop IV: Big Data in Multi-Messenger Astrophysics. Abstract: The data from the gravitational wave detectors are non-stationary an
From playlist Workshop: Big Data in Multi-Messenger Astrophysics
Lecture 10 - Decision Trees and Ensemble Methods | Stanford CS229: Machine Learning (Autumn 2018)
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GdlrqJ Raphael Townshend PhD Candidate and CS229 Head TA To follow along with the course schedule and syllabus, visit: http://cs229.stanford.edu/syllabus-autumn
From playlist Stanford CS229: Machine Learning Full Course taught by Andrew Ng | Autumn 2018
Generalized Linear Model (Part B)
Regression Analysis by Dr. Soumen Maity,Department of Mathematics,IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in
From playlist IIT Kharagpur: Regression Analysis | CosmoLearning.org Mathematics
Determine a Least Squares Solutions to Ax=b
This video explains how to determine a least-squares solutions to Ax=b that has no solution.
From playlist Least Squares Solutions