Linear Algebra - Least Squares Approximation - 03 - Fitting data to a straight curve Part 1
Channel: Arthur Parzygnat
Date: 2019-01-08

The least squares approximation theorem is used to fit data to a straight line in a general setting. Note: the column space of A is 2-dimensional provided there are at least two different values for the x's (this is mentioned in more detail in the next video).

This is part of a series of lectures on special topics in linear algebra https://www.youtube.com/playlist?list=PLSx1kJDjrLRSS4Aio9WfQlNnH6X6pC_8t. It is assumed the viewer has taken (or is well into) a course in linear algebra. Some topics may also require additional background. Topics covered include linear regression and data analysis, ordinary linear differential equations, differential operators, function spaces, category-theoretic aspects of linear algebra, support vector machines (machine learning), Hamming's error-correcting codes, stochastic maps and Markov chains, tensor products, finite-dimensional C*-algebras, algebraic probability theory, completely positive maps, aspects of quantum information theory, and more.

The next video on Special Topics in Linear Algebra is on Fitting data to a straight curve Part 2: https://youtu.be/uFpRy3jWmQc

The previous video on Special Topics in Linear Algebra is on a fundamental theorem for least squares: https://youtu.be/6YM-TOiVcH0

These videos were created during the 2019 Spring semester at the UConn CETL Lightboard Room.