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Linear Algebra: Orthogonality and Diagonalization

kurs ist nicht verfügbar in Deutsch (Deutschland)

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Linear Algebra: Orthogonality and Diagonalization

Dieser Kurs ist Teil von Spezialisierung „Linear Algebra from Elementary to Advanced“

Dozent: Joseph W. Cutrone, PhD

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4 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

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Einige einschlägige Kenntnisse erforderlich

9 Stunden zu vervollständigen

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

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Kategorie: Applied Mathematics
Kategorie: Linear Algebra
Kategorie: Artificial Intelligence and Machine Learning (AI/ML)
Kategorie: Algebra
Kategorie: Geometry

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Dieser Kurs ist Teil der Spezialisierung Spezialisierung „Linear Algebra from Elementary to Advanced“

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In diesem Kurs gibt es 4 Module

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.

Moduldetails

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.

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2 Videos2 Lektüren3 Aufgaben

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.

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3 Videos3 Lektüren4 Aufgaben

3 VideosInsgesamt 51 Minuten

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

3 LektürenInsgesamt 30 Minuten

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

4 AufgabenInsgesamt 120 Minuten

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

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.