An Introduction to Linear Regression Analysis
Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class. Playlist on Linear Regression http://www.youtube.com/course?list=ECF596A4043DBEAE9C Like us on: http://www.facebook.com/PartyMoreStudyLess Created by David Lon
From playlist Linear Regression.
Gradient Boost Part 1 (of 4): Regression Main Ideas
Gradient Boost is one of the most popular Machine Learning algorithms in use. And get this, it's not that complicated! This video is the first part in a series that walks through it one step at a time. This video focuses on the main ideas behind using Gradient Boost to predict a continuous
From playlist StatQuest
Logistic Regression - VISUALIZED!
People talk about "sigmoid functions", "decision boundaries" and “Training”. But what exactly is happening behind the scenes? Let’s see for ourselves! Please SUBSCRIBE to me for more content! Shoutout to 3blue1brown for creating his animation math engine “manim”. Give this a * on your
From playlist Logistic Regression
Gradient Boost Part 2 (of 4): Regression Details
Gradient Boost is one of the most popular Machine Learning algorithms in use. And get this, it's not that complicated! This video is the second part in a series that walks through it one step at a time. This video focuses on the original Gradient Boost algorithm used to predict a continuou
From playlist StatQuest
Regression Trees, Clearly Explained!!!
Regression Trees are one of the fundamental machine learning techniques that more complicated methods, like Gradient Boost, are based on. They are useful for times when there isn't an obviously linear relationship between what you want to predict, and the things you are using to make the p
From playlist StatQuest
Logistic Regression - Is it Linear Regression?
Is it Linear? Why the sigmoid? Let's talk about it. Breaking Linear Regression video: https://www.youtube.com/watch?v=Bu1WCOQpBnM RESOURCES [1] Great Lecture notes to start understanding Logistic Regression: https://pages.stat.wisc.edu/~st849-1/lectures/GLMH.pdf [2] More slightly detaile
From playlist Logistic Regression
(ML 9.2) Linear regression - Definition & Motivation
Linear regression arises naturally from a sequence of simple choices: discriminative model, Gaussian distributions, and linear functions. A playlist of these Machine Learning videos is available here: http://www.youtube.com/view_play_list?p=D0F06AA0D2E8FFBA
From playlist Machine Learning
Recordings of the corresponding course on Coursera. If you are interested in exercises and/or a certificate, have a look here: https://www.coursera.org/learn/pca-machine-learning
From playlist Projections
Fast and optimal low-rank tensor regression via importance - Garvesh Raskutti, UW-Madison
Recent years have witnessed an increased cross-fertilisation between the fields of statistics and computer science. In the era of Big Data, statisticians are increasingly facing the question of guaranteeing prescribed levels of inferential accuracy within certain time budget. On the other
From playlist Statistics and computation
Finding structure in high dimensional data, methods and fundamental limitations - Boaz Nadler
Members' Seminar Topic: Finding structure in high dimensional data, methods and fundamental limitations Speaker: Boaz Nadler Affiliation: Weizmann Institute of Science; Member, School of Mathematics Date: October 14, 2019 For more video please visit http://video.ias.edu
From playlist Mathematics
Linear regression is used to compare sets or pairs of numerical data points. We use it to find a correlation between variables.
From playlist Learning medical statistics with python and Jupyter notebooks
Laurent Jacques/Valerio Cambareri: Small width, low distortions: quantized random projections of...
Laurent Jacques / Valerio Cambareri: Small width, low distortions: quantized random projections of low-complexity signal sets Abstract: Compressed sensing theory (CS) shows that a "signal" can be reconstructed from a few linear, and most often random, observations. Interestingly, this rec
From playlist HIM Lectures: Trimester Program "Mathematics of Signal Processing"
Stanley Osher: "Linearized Bregman Algorithm for L1-regularized Logistic Regression"
Graduate Summer School 2012: Deep Learning, Feature Learning "Linearized Bregman Algorithm for L1-regularized Logistic Regression" Stanley Osher, UCLA Institute for Pure and Applied Mathematics, UCLA July 20, 2012 For more information: https://www.ipam.ucla.edu/programs/summer-schools/g
From playlist GSS2012: Deep Learning, Feature Learning
Tutorial 3.3: Lorenzo Rosasco - Machine Learning Part 3
MIT RES.9-003 Brains, Minds and Machines Summer Course, Summer 2015 View the complete course: https://ocw.mit.edu/RES-9-003SU15 Instructor: Lorenzo Rosasco Machine learning methods used in intelligence science & data science. Basic concepts & theory of various learning methods, and applic
From playlist MIT RES.9-003 Brains, Minds and Machines Summer Course, Summer 2015
Secular Humanism: Mortality and Meaning - Dwight H. Terry Lectures 2013
Philosophy professor Philip Kitcher delivers the third of four lectures on secular humanism. Kitcher, who was born in London in 1947, received his B.A. from Cambridge University and his Ph.D. from Princeton. He has taught at several American universities and is currently John Dewey Profe
From playlist Terry Lectures
Session 4 | 24.261 Philosophy of Love in the Western World
Appraisal and bestowal, Derrida, freedom versus paternalism, love of things, artificial intelligence View the complete course at: http://ocw.mit.edu/24-261F04 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 24.261 Philosophy of Love in the Western World
Workshop context setting; Phase transitions in distributed by Partha Mitra
Statistical Physics Methods in Machine Learning DATE: 26 December 2017 to 30 December 2017 VENUE: Ramanujan Lecture Hall, ICTS, Bengaluru The theme of this Discussion Meeting is the analysis of distributed/networked algorithms in machine learning and theoretical computer science in the "t
From playlist Statistical Physics Methods in Machine Learning
Deep dictionary learning approaches for image super-resolution - Pier Luigi Dragotti, Imperial
This workshop - organised under the auspices of the Isaac Newton Institute on “Approximation, sampling and compression in data science” — brings together leading researchers in the general fields of mathematics, statistics, computer science and engineering. About the event The workshop ai
From playlist Mathematics of data: Structured representations for sensing, approximation and learning
Logistic Regression Details Pt1: Coefficients
When you do logistic regression you have to make sense of the coefficients. These are based on the log(odds) and log(odds ratio), but, to be honest, the easiest way to make sense of these are through examples. In this StatQuest, I walk you though two Logistic Regression Examples, step-by-s
From playlist StatQuest
This talk will give an overview of the various optimization functions that can be used to solve a wide variety of convex, nonconvex and multidomain problems. The Wolfram optimization functionality will be demonstrated using a diverse set of examples. Visiting this talk will enable you to s
From playlist Wolfram Technology Conference 2022