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
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From playlist Statistical 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
Ex: Comparing Linear and Exponential Regression
This video provides an example on how to perform linear regression and exponential regression on the TI84. The best model is identified based up the value of R^2. Site: http://mathispower4u.com Blog: http://mathispower4u.wordpress.com
From playlist Solving Applications Using Exponential Equations / Compounded and Continuous Interest / Exponential Regression
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
Veronika Ročková: Bayesian Spatial Adaptation
CIRM VIRTUAL EVENT Recorded during the meeting "Mathematical Methods of Modern Statistics 2" the June 09, 2020 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians
From playlist Virtual Conference
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.
(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
Yoshua Bengio: "Representation Learning and Deep Learning, Pt. 2"
Graduate Summer School 2012: Deep Learning Feature Learning "Representation Learning and Deep Learning, Pt. 2" Yoshua Bengio, University of Montreal Institute for Pure and Applied Mathematics, UCLA July 16, 2012 For more information: https://www.ipam.ucla.edu/programs/summer-schools/gra
From playlist GSS2012: Deep Learning, Feature Learning
Strata 2012: Hal Varian, "Using Google Data for Short-term Economic Forecasting"
Google Insights for Search provides an index of search activity for millions of queries. These queries can sometimes help understand consumer behavior. Hal describes some of the issues that arise in trying to use this data for short-term economic forecasts and provide examples. Hal Varian
From playlist Strata SC 2012
Optical Imaging and Analysis of Neuronal and Astrocyte Activity....(Lecture 1) by Misha Ahrens
PROGRAM ICTP-ICTS WINTER SCHOOL ON QUANTITATIVE SYSTEMS BIOLOGY (ONLINE) ORGANIZERS: Vijaykumar Krishnamurthy (ICTS-TIFR, India), Venkatesh N. Murthy (Harvard University, USA), Sharad Ramanathan (Harvard University, USA), Sanjay Sane (NCBS-TIFR, India) and Vatsala Thirumalai (NCBS-TIFR,
From playlist ICTP-ICTS Winter School on Quantitative Systems Biology (ONLINE)
Eyal Lubetzky - Entropic repulsion in 3D Ising
Fifty years ago, Dobrushin famously showed that the 3D Ising interface on a cylinder with plus/minus boundary condition is rigid. By now there is detailed understanding of the (2+1)D Solid-On-Solid model that approximates said interface, and notably, its entropic repulsion phenomenon above
From playlist 100…(102!) Years of the Ising Model
Kerrie Mengersen: Bayesian Modelling
Abstract: This tutorial will be a beginner’s introduction to Bayesian statistical modelling and analysis. Simple models and computational tools will be described, followed by a discussion about implementing these approaches in practice. A range of case studies will be presented and possibl
From playlist Probability and Statistics
Aki Vehtari: Model assessment, selection and averaging
Abstract: The tutorial covers cross-validation, and projection predictive approaches for model assessment, selection and inference after model selection and Bayesian stacking for model averaging. The talk is accompanied with R notebooks using rstanarm, bayesplot, loo, and projpred packages
From playlist Probability and Statistics
Jacob Bernstein - Entropy and C^0 stability of hypersurfaces - IPAM at UCLA
Recorded 09 February 2022. Jacob Bernstein of Johns Hopkins University presents "Entropy and C^0 stability of hypersurfaces" at IPAM's Calculus of Variations in Probability and Geometry Workshop. Abstract: Colding and Minicozzi have introduced a natural measure of the complexity of a subm
From playlist Workshop: Calculus of Variations in Probability and Geometry
Time Series Analysis with the KNIME Analytics Platform
In this session, you’ll learn about the main concepts behind Time Series: preprocessing, alignment, missing value imputation, forecasting, and evaluation. Together we will build a demand prediction application: first with (S)ARIMA models and then with machine learning models. The codeless
From playlist Advanced Machine Learning
Least squares method for simple linear regression
In this video I show you how to derive the equations for the coefficients of the simple linear regression line. The least squares method for the simple linear regression line, requires the calculation of the intercept and the slope, commonly written as beta-sub-zero and beta-sub-one. Deriv
From playlist Machine learning
Applying Exponential Models // Math Minute [#34] [ALGEBRA]
Exponential functions work a lot like linear functions. There are typically two parameters that guide the use of the exponential function: the initial value (like the y-intercept of a linear function) and the factor of growth (like the slope of a linear function). There are some additional
From playlist Math Minutes
Macaroon, Macaron, Macaroni: The Secret Language of Food with Dan Jurafsky
Why does "macaroon" sound like "macaroni"? Did ketchup really come from China? Do the adjectives on a menu predict how much your dinner will cost? Do men and women use different words in restaurant reviews? The language we use to talk about food offers surprising insights on world history
From playlist Reunion Homecoming
Exponential Regression on the TI84 - Example 1
http://mathispower4u.wordpress.com/
From playlist TI-84: Regression on the Graphing Calculator