Linear Algebra from Elementary to Advanced Specialization
Learn Linear Algebra - the Theory of Everything!. Master techniques and theory of linear algebra
Instructor: Joseph W. Cutrone, PhD
Skills you'll gain
Specialization - 3 course series
Learners will have the opportunity to complete special projects in the course. Projects include exploration of advanced topics in mathematics and their relevant applications. Project topics include Markov Chains, the Google PageRank matrix, and recursion removal using eigenvalues.
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. .
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.
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.
Linear Algebra: Matrix Algebra, Determinants, & Eigenvectors
Linear Algebra: Orthogonality and Diagonalization
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