Linear Algebra from Elementary to Advanced

Johns Hopkins University via Coursera Specialization

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Overview

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This specialization is a three course sequence that will cover the main topics of undergraduate linear algebra. Defined simply, linear algebra is a branch of mathematics that studies vectors, matrices, lines and the areas and spaces they create. These concepts are foundational to almost every industry and discipline, giving linear algebra the informal name "The Theory of Everything". This specialization assumes no prior knowledge of linear algebra and requires no calculus or similar courses as a prerequisite. The first course starts with the study of linear equations and matrices. Matrices and their properties, such as the determinant and eigenvalues are covered. The specialization ends with the theory of symmetric matrices and quadratic forms. Theory, applications, and examples are presented throughout the course. Examples and pictures are provided in low dimensions before abstracting to higher dimensions. An equal emphasis is placed on both algebraic manipulation as well as geometric understanding of the concepts of linear algebra. Upon completion of this specialization , students will be prepared for advanced topics in data science, AI, machine learning, finance, mathematics, computer science, or economics.

Syllabus

Course 1: Linear Algebra: Linear Systems and Matrix Equations
Course 2: Linear Algebra: Matrix Algebra, Determinants, & Eigenvectors
Course 3: Linear Algebra: Orthogonality and Diagonalization

Courses

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14 hours 53 minutes
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This course is the second course in the Linear Algebra Specialization. In this course, we continue to develop the techniques and theory to study matrices as special linear transformations (functions) on vectors. In particular, we develop techniques to manipulate matrices algebraically. This will allow us to better analyze and solve systems of linear equations. Furthermore, the definitions and theorems presented in the course allow use to identify the properties of an invertible matrix, identify relevant subspaces in R^n, We then focus on the geometry of the matrix transformation by studying the eigenvalues and eigenvectors of matrices. These numbers are useful for both pure and applied concepts in mathematics, data science, machine learning, artificial intelligence, and dynamical systems. We will see an application of Markov Chains and the Google PageRank Algorithm at the end of the course.
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10 hours 9 minutes
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This is the first course of a three course specialization that introduces the students to the concepts of linear algebra, one of the most important and basic areas of mathematics, with many real-life applications. This foundational material provides both theory and applications for topics in mathematics, engineering and the sciences. The course content focuses on linear equations, matrix methods, analytical geometry and linear transformations. As well as mastering techniques, students will be exposed to the more abstract ideas of linear algebra. Lectures, readings, quizzes, and a project all help students to master course content and and learn to read, write, and even correct mathematical proofs. At the end of the course, students will be fluent in the language of linear algebra, learning new definitions and theorems along with examples and counterexamples. Students will also learn to employ techniques to classify and solve linear systems of equations. This course prepares students to continue their study of linear transformations with the next course in the specialization. .
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9 hours 15 minutes
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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.