18.06SC Linear Algebra (Fall 2011, MIT OCW): Lecture 17 - Projection Matrices and Least Squares

18.06SC Linear Algebra

18.06SC Linear Algebra (Fall 2011, MIT OCW). Taught by Prof. Gilbert Strang, this course covers matrix theory and linear algebra, emphasizing topics useful in other disciplines such as physics, economics and social sciences, natural sciences, and engineering. This course has been designed for independent study. It provides everything you will need to understand the concepts covered in the course. The materials include: a complete set of lecture videos, summary notes, problem solving videos, and a full set of exams and solutions. (from ocw.mit.edu)

Lecture 17 - Projection Matrices and Least Squares

Linear regression is commonly used to fit a line to a collection of data. The method of least squares can be viewed as finding the projection of a vector. Linear algebra provides a powerful and efficient description of linear regression in terms of the matrix A^TA.

References

Projection Matrices and Least Squares
The method of least squares can be viewed as finding the projection of a vector. Lecture Video and Summary. Suggested Reading. Problem Solving Video.

Go to the Course Home or watch other lectures:

I. Ax=b and the Four Subspaces

Lecture 01 - The Geometry of Linear Equations

Lecture 02 - An Overview of Key Ideas

Lecture 03 - Elimination with Matrices

Lecture 04 - Multiplication and Inverse Matrices

Lecture 05 - Factorization into A = LU

Lecture 06 - Transposes, Permutations, Vector Spaces

Lecture 07 - Column Space and Nullspace

Lecture 08 - Solving Ax = 0: Pivot Variables, Special Solutions

Lecture 09 - Solving Ax = b: Row Reduced form R

Lecture 10 - Independence, Basis and Dimension