This applied mathematics textbook covers Matrices and Pathways, Statistics and Probability, Finance, Cyclic, Recursive and Fractal Patterns, Vectors, and Design. The approach used is primarily data driven, using numerical and geometrical problem-solving techniques.

The main objective of this lesson is to illustrate an important application of mathematics in practical life -- namely in art. Most of the pictures selected for this lesson are visible on the walls of Al-Hambra – Granada (Spain), which is one of the most important landmarks in the Islamic civilization. There are three educational goals for this lesson: (1) establishing the concept of isometries; (2) giving real-life examples of groups; (3) demonstrating the importance of matrices and their applications. As background for this lesson, students just need some familiarity with the concept of a group and a limited knowledge about matrices and the inverse of a non-singular matrix.

This is a variation on 18.02 Multivariable Calculus. It covers the same topics as in 18.02, but with more focus on mathematical concepts.
Acknowledgement
Prof. McKernan would like to acknowledge the contributions of Lars Hesselholt to the development of this course.

Sal discusses the conditions of matrix dimensions for which addition or multiplication are defined. Created by Sal Khan.

This course is is a collection of resources on OER Commons curated for Adult Education instructors and students to show the integration of math into the Information Technology Career Sector. Students will analyze and practice specific skills related to being in IT as well as develop math skills. Modules in this curriculum guide can be studied in any particular order as one does not necessarily build upon the other. Each includes the idea of building mathematical and logic skills required for programming and other IT related careers.

This Intermediate Algebra textbook was developed with consideration of neuroscience principles about learning. It is organized around the concepts of 'solving' and 'graphing'. The problem sets incorporate distributed and mixed practice to promote long term memory formation for the concepts and procedures involved in each section.

Matlab es un lenguaje de programación matemática que sirve para realizar cálculos algebraicos, así como también para programar y ejecutar simulaciones, modelos y aplicaciones de ingeniería, basándose fundamentalmente en la utilización de matrices. Matlab es un entorno computacional aplicado a varias áreas de la ciencia moderna como:
- Matemáticas, estadísticas y optimización.
- Diseño y análisis de sistemas de control.
- Procesamiento de señales espectrales en comunicaciones.
- Procesamiento de imágenes y visión computacional.
- Medición y verificación de bases de datos.
- Finanzas computacionales.
- Biología computacional.
- Mecatrónica.
- Robótica.
- Generación de códigos de programación C++.
- Desarrollo de aplicativos computacionales.
- Conectividad de bases de datos.
- Simulación gráfica de imágenes satelitáles.

This course is intended to assist undergraduates with learning the basics of programming in general and programming MATLAB® in particular.

Sal checks whether the commutative property applies for matrix multiplication. In other words, he checks whether for any two matrices A and B, A*B=B*A (the answer is NO, by the way). Created by Sal Khan.

Learn Differential Equations: Up Close with _Gilbert Strang and_ Cleve Moler is an in-depth series of videos about differential equations and the MATLAB® ODE suite. These videos are suitable for students and life-long learners to enjoy.
About the Instructors
Gilbert Strang is the MathWorks Professor of Mathematics at MIT. His research focuses on mathematical analysis, linear algebra and PDEs. He has written textbooks on linear algebra, computational science, finite elements, wavelets, GPS, and calculus.
Cleve Moler is chief mathematician, chairman, and cofounder of MathWorks. He was a professor of math and computer science for almost 20 years at the University of Michigan, Stanford University, and the University of New Mexico.
These videos were produced by The MathWorks and are also available on The MathWorks website.

This course offers a rigorous treatment of linear algebra, including vector spaces, systems of linear equations, bases, linear independence, matrices, determinants, eigenvalues, inner products, quadratic forms, and canonical forms of matrices. Compared with 18.06 Linear Algebra, more emphasis is placed on theory and proofs.

This 27-minute video lesson provides an example using the orthogonal change-of-basis matrix to find the transformation matrix. [Linear Algebra playlist: Lesson 128 of 143]

This 17-minute video lesson shows how to express a projection on to a line as a Matrix Vector prod. [Linear Algebra playlist: Lesson 61 of 143]

This 12-minute video lesson provides an introduction to the null space of a matrix and shows that the null space of a matrix is a valid subspace. [Linear Algebra playlist: Lesson 34 of 143]

This 18-minute video lesson shows how to solve a system of linear equations by putting an augmented matrix into reduced row echelon form. [Linear Algebra playlist: Lesson 30 of 143]

This 12-minute video lesson provides another example of solving a system of linear equations by putting an augmented matrix into reduced row echelon form. [Linear Algebra playlist: Lesson 32 of 143]

This 13-minute video lesson shows how to calculate the null space of a matrix. [Linear Algebra playlist: Lesson 35 of 143]

This 12-minute video lesson discusses how to understand how the null space of a matrix relates to the linear independence of its column vectors. [Linear Algebra playlist: Lesson 36 of 143]

This 11-minute video lesson shows that orthogonal matrices preserve angles and lengths. [Linear Algebra playlist: Lesson 129 of 143]