Generalized functional linear model

03 01 Part 1 of 1 Generalized Linear Models

From playlist Coursera Regression V2

PDE FIND

We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The regression framework relies on sparsity promoting techniques to select the nonlinear and partial derivative

From playlist Research Abstracts from Brunton Lab

03 04 Hodgepodge

From playlist Coursera Regression V2

(ML 13.6) Graphical model for Bayesian linear regression

As an example, we write down the graphical model for Bayesian linear regression. We introduce the "plate notation", and the convention of shading random variables which are being conditioned on.

From playlist Machine Learning

Valeria Simoncini: Computational methods for large-scale matrix equations and application to PDEs

Linear matrix equations such as the Lyapunov and Sylvester equations and their generalizations have classically played an important role in the analysis of dynamical systems, in control theory and in eigenvalue computation. More recently, matrix equations have emerged as a natural linear a

From playlist Numerical Analysis and Scientific Computing

10g Machine Learning: Isotonic Regression

Lecture on isotonic regression. Introduces the idea of a piece-wise linear model with monotonic constraint. Follow along with the demonstration workflow: https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/SubsurfaceDataAnalytics_IsotonicRegression.ipynb

From playlist Machine Learning

(8.1) A General Approach to Nonlinear Differential Questions

This video briefly describes the approach to gaining information about the solution to nonlinear differential equations. https://mathispower4u.com

From playlist Differential Equations: Complete Set of Course Videos

(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

C34 Expanding this method to higher order linear differential equations

I this video I expand the method of the variation of parameters to higher-order (higher than two), linear ODE's.

From playlist Differential Equations

Stanford CS229: Machine Learning | Summer 2019 | Lecture 6 - Exponential Family & GLM

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3Eb7mIi 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)

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

Deep Learning Lecture 1.2 - Intro Shallow ML

Deep Learning Lecture Intro: - Shallow ML - Learning Problem - Linear Least Squares - Hyperparameter Selection

From playlist Deep Learning Lecture

Statistical Rethinking 2022 Lecture 03 - Geocentric Models

Linear regression from a Bayesian perspective Slides and course materials: https://github.com/rmcelreath/stat_rethinking_2022 Music Intro: https://www.youtube.com/watch?v=4y33h81phKU Flow: https://www.youtube.com/watch?v=ip4n8zaTg1w Pause: https://www.youtube.com/watch?v=1f-NQAgm-YM Cha

From playlist Statistical Rethinking 2022

StatsLearning Chapter 7 - part 4

From playlist ISLR Chapter 7: Moving Beyond Linearity

Model Invariants and Functional Regularization

SIAM Activity Group on FME Virtual Talk Series Join us for a series of online talks on topics related to mathematical finance and engineering and running every two weeks until further notice. The series is organized by the SIAM Activity Group on Financial Mathematics and Engineering. Spea

From playlist SIAM Activity Group on FME Virtual Talk Series

Statistical Learning: 7.1 Polynomials and Step Functions

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: xhttps://www.edx.org/course/statistical-learning

From playlist Statistical Learning

Optimisation - an introduction: Professor Coralia Cartis, University of Oxford

Coralia Cartis (BSc Mathematics, Babesh-Bolyai University, Romania; PhD Mathematics, University of Cambridge (2005)) has joined the Mathematical Institute at Oxford and Balliol College in 2013 as Associate Professor in Numerical Optimization. Previously, she worked as a research scientist

From playlist Data science classes

Column view of a matrix

In this tutorial I put emphasis of the column view of a matrix of coefficients. We are used to the row view when it comes to systems of linear equations, but it is the column view that is much more fascinating. The column view helps us view a system of linear equations as vectors in a sp

From playlist Introducing linear algebra

Efficient Zero Knowledge Proofs - A Modular Approach (Lecture 2) by Yuval Ishai

DISCUSSION MEETING : FOUNDATIONAL ASPECTS OF BLOCKCHAIN TECHNOLOGY ORGANIZERS : Pandu Rangan Chandrasekaran DATE : 15 to 17 January 2020 VENUE : Madhava Lecture Hall, ICTS, Bangalore Blockchain technology is among one of the most influential disruptive technologies of the current decade.

From playlist Foundational Aspects of Blockchain Technology 2020