Become a Linear Algebra Master
Become a Linear Algebra Master
Become a Master of Linear Algebra. Learn everything you need to know on Linear Algebra and be a master in itRating: 0.0 out of 50 reviews6 total hours40 ...
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Linear algebra is a branch of mathematics that deals with linear equations and linear transformations. It is an important subject in a wide range of fields, including physics, engineering, computer science, and data science.
A course on "Become a Linear Algebra Master" would likely teach students the fundamental concepts and techniques of linear algebra. This could include topics such as vector algebra, matrix algebra, systems of linear equations, eigenvalues and eigenvectors, and applications of linear algebra in various fields.
The course may also cover more advanced topics such as multivariate calculus, optimization, and the use of linear algebra in machine learning and data analysis.
Overall, a course on "Become a Linear Algebra Master" would be an excellent opportunity for anyone interested in learning about linear algebra and its applications. It would provide a comprehensive understanding of the concepts and techniques of linear algebra, and would equip students with the knowledge and skills needed to succeed in this field.
What you'll learn
- Operations on one matrix, including solving linear systems, and Gauss-Jordan elimination
- Operations on two matrices, including matrix multiplication and elimination matrices
- Matrices as vectors, including linear combinations and span, linear independence, and subspaces
- Dot products and cross products, including the Cauchy-Schwarz and vector triangle inequalities
- Matrix-vector products, including the null and column spaces, and solving Ax=b
- Transformations, including linear transformations, projections, and composition of transformations
- Inverses, including invertible and singular matrices, and solving systems with inverse matrices
- Determinants, including upper and lower triangular matrices, and Cramer's rule
- Transposes, including their determinants, and the null (left null) and column (row) spaces of the transpose
- Orthogonality and change of basis, including orthogonal complements, projections onto a subspace, least squares, and changing the basis
- Orthonormal bases and Gram-Schmidt, including definition of the orthonormal basis, and converting to an orthonormal basis with the Gram-Schmidt process
- Eigenvalues and Eigenvectors, including finding eigenvalues and their associate eigenvectors and eigenspaces, and eigen in three dimensions
