Linear Algebra: Orthogonality and Diagonalization

Linear Algebra: Orthogonality and Diagonalization

This course is part of Linear Algebra from Elementary to Advanced Specialization

Instructor: Joseph W. Cutrone, PhD

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4 modules

Gain insight into a topic and learn the fundamentals.

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Intermediate level

Some related experience required

9 hours to complete

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

50 reviews

Intermediate level

Some related experience required

9 hours to complete

Flexible schedule

Learn at your own pace

Skills you'll gain

Details to know

Shareable certificate

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Assessments

11 assignments

Taught in English

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This course is part of the Linear Algebra from Elementary to Advanced Specialization

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There are 4 modules in this course

This is the third and final course in the Linear Algebra Specialization that focuses on the theory and computations that arise from working with orthogonal vectors. This includes the study of orthogonal transformation, orthogonal bases, and orthogonal transformations. The course culminates in the theory of symmetric matrices, linking the algebraic properties with their corresponding geometric equivalences. These matrices arise more often in applications than any other class of matrices.

The theory, skills and techniques learned in this course have applications to AI and machine learning. In these popular fields, often the driving engine behind the systems that are interpreting, training, and using external data is exactly the matrix analysis arising from the content in this course. Successful completion of this specialization will prepare students to take advanced courses in data science, AI, and mathematics.

In this module, we define a new operation on vectors called the dot product. This operation is a function that returns a scalar related to the angle between the vectors, distance between vectors, and length of vectors. After working through the theory and examples, we hone in on both unit (length one) and orthogonal (perpendicular) vectors. These special vectors will be pivotal in our course as we start to define linear transformations and special matrices that use only these vectors.

What's included

2 videos2 readings3 assignments

In this module we will study the special type of transformation called the orthogonal projection. We have already seen the formula for the orthogonal projection onto a line so now we generalize the formula to the case of projection onto any subspace W. The formula will require basis vectors that are both orthogonal and normalize and we show, using the Gram-Schmidt Process, how to meet these requirements given any non-empty basis.

What's included

3 videos3 readings4 assignments

3 videosTotal 51 minutes

Orthogonal Projections18 minutes
Gram-Schmidt Process14 minutes
Least-Squares Problems19 minutes

3 readingsTotal 30 minutes

Orthogonal Projections10 minutes
Finding Orthogonal Bases10 minutes
Least-Squares Solutions10 minutes

4 assignmentsTotal 120 minutes

Orthogonal Projections and Least Squares30 minutes
Orthogonal Projections Practice30 minutes
Orthogonal Bases Practice30 minutes
Least-Squares Solutions Practice30 minutes

In this module we look to diagonalize symmetric matrices. The symmetry displayed in the matrix A turns out to force a beautiful relationship between the eigenspaces. The corresponding eigenspaces turn out to be mutually orthogonal. After normalizing, these orthogonal eigenvectors give a very special basis of R^n with extremely useful applications to data science, machine learning, and image processing. We introduce the notion of quadratic forms, special functions of degree two on vectors , which use symmetric matrices in their definition. Quadratic forms are then completely classified based on the properties of their eigenvalues.