In statistics, an additive model (AM) is a nonparametric regression method. It was suggested by Jerome H. Friedman and Werner Stuetzle (1981) and is an essential part of the ACE algorithm. The AM uses a one-dimensional smoother to build a restricted class of nonparametric regression models. Because of this, it is less affected by the curse of dimensionality than e.g. a p-dimensional smoother. Furthermore, the AM is more flexible than a standard linear model, while being more interpretable than a general regression surface at the cost of approximation errors. Problems with AM, like many other machine learning methods, include model selection, overfitting, and multicollinearity. (Wikipedia).

Linear regression

Linear regression is used to compare sets or pairs of numerical data points. We use it to find a correlation between variables.

Exponential Growth Models

Introduces notation and formulas for exponential growth models, with solutions to guided problems.

From playlist Discrete Math

(ML 16.7) EM for the Gaussian mixture model (part 1)

Applying EM (Expectation-Maximization) to estimate the parameters of a Gaussian mixture model. Here we use the alternate formulation presented for (unconstrained) exponential families.

From playlist Machine Learning

Linear regression ANOVA ANCOVA Logistic Regression

In this video tutorial you will learn about the fundamentals of linear modeling: linear regression, analysis of variance, analysis of covariance, and logistic regression. I work through the results of these tests on the white board, so no code and no complicated equations. Linear regressi

From playlist Statistics

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

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.

Linear Regression using Python

This seminar series looks at four important linear models (linear regression, analysis of variance, analysis of covariance, and logistic regression). A video that explains all four model types is at https://www.youtube.com/watch?v=SV9AxXFWZnM&t=12s This video is on linear regression usin

From playlist Statistics

Statistical Learning: 7.4 Generalized Additive Models and Local Regression

Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion: https://www.edx.org/course/statistical-learning

From playlist Statistical Learning

Alison Etheridge & Nick Barton: Applying the infinitesimal model

The infinitesimal model is based on the assumption that, conditional on the pedigree, the joint distribution of trait values is multivariate normal, then, selecting parents does not alter the variance amongst offspring. We explain how the infinitesimal model extends to include dominance as

From playlist Probability and Statistics

Compare Linear and Exponential Functions

This video compares linear and exponential functions. http://mathispower4u.com

From playlist Introduction to Exponential Functions

Matrix Models, Gauge-Gravity Duality, and Simulations on the Lattice (Lecture 3) by Georg Bergner

PROGRAM NONPERTURBATIVE AND NUMERICAL APPROACHES TO QUANTUM GRAVITY, STRING THEORY AND HOLOGRAPHY (HYBRID) ORGANIZERS: David Berenstein (University of California, Santa Barbara, USA), Simon Catterall (Syracuse University, USA), Masanori Hanada (University of Surrey, UK), Anosh Joseph (II

From playlist NUMSTRING 2022

Data Science - Part X - Time Series Forecasting

For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This lecture provides an overview of Time Series forecasting techniques and the process of creating effective for

From playlist Data Science

Andrew Ahn (Columbia) -- Airy edge fluctuations in random matrix sums

In this talk, we discuss a novel integrable probability approach to access edge fluctuations in sums of unitarily invariant Hermitian matrices. We focus on a particular regime where the number of summands is large (but fixed) under which the Airy point process appears. The approach is base

From playlist Columbia Probability Seminar

22. Structure of set addition II: groups of bounded exponent and modeling lemma

MIT 18.217 Graph Theory and Additive Combinatorics, Fall 2019 Instructor: Yufei Zhao View the complete course: https://ocw.mit.edu/18-217F19 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP62qauV_CpT1zKaGG_Vj5igX Prof. Zhao explains the Ruzsa covering lemma and uses it

GPT3: An Even Bigger Language Model - Computerphile

Basic mathematics from a language model? Rob Miles on GPT3, where it seems like size does matter! More from Rob Miles: http://bit.ly/Rob_Miles_YouTube https://www.facebook.com/computerphile https://twitter.com/computer_phile This video was filmed and edited by Sean Riley. Compu

From playlist Computerphile Videos

Matrix Models, Gauge-Gravity Duality, and Simulations on the Lattice (Lecture 2) by Georg Bergner

NONPERTURBATIVE AND NUMERICAL APPROACHES TO QUANTUM GRAVITY, STRING THEORY AND HOLOGRAPHY (HYBRID) ORGANIZERS: David Berenstein (University of California, Santa Barbara, USA), Simon Catterall (Syracuse University, USA), Masanori Hanada (University of Surrey, UK), Anosh Joseph (IISER Mohal

From playlist NUMSTRING 2022

Introduction to quantitative genetics..... by Maria Orive

ORGANIZERS : Deepa Agashe and Kavita Jain DATE & TIME : 05 March 2018 to 17 March 2018 VENUE : Ramanujan Lecture Hall, ICTS Bangalore No living organism escapes evolutionary change. Evolutionary biology thus connects all biological disciplines. To understand the processes drivin