
Explore the vector space concept by detailing addition and scalar multiplication and outlining the vector space axioms, including closure, commutativity, associativity, and identity with examples.
Explore finite dimensional spaces by defining bases with finite length and proving the basis theorem: any linearly independent set has length at most the spanning set, with practical examples.
Explore ordered bases as fixed sequences of linearly independent vectors and learn to compute coordinates of vectors and polynomials relative to any basis, using linear combinations and matrix methods.
Learn how to represent a linear transformation as a matrix relative to a basis, including the standard basis, and apply it to practical examples.
Explore the dual space of a finite-dimensional vector space, V*, the space of all linear functionals. Build a basis of V* by defining functionals on a basis and extending linearly.
Explore the superposition principle for differential equations, showing how linear combinations of solutions yield new solutions, and introduce the Wronskian to test linear independence of functions.
Explore second order differential equations with constant coefficients, solve via the auxiliary equation, and classify solutions for distinct real, repeated, and complex roots with examples.
Identify fundamental, linearly independent solutions for a second-order linear differential equation, show any solution is a linear combination of these, and establish the unique solution via initial conditions.
Explore eigenvalues and eigenvectors of linear operators by solving A v = lambda v and det(A - lambda I) = 0, with a 2x2 example yielding eigenvalues 1 and -1.
Learn how Markov chains model a sequence of dependent random events with state vectors and transition probabilities, illustrated by weather examples.
Explains the dot product in R^n, defines the vector norm and distance, and derives the angle between vectors via cosine, linking inner product, length, and orientation.
Explore projection in inner product spaces and the constructive method to build an orthonormal basis in any finite dimensional space.
Define the orthogonal complement in an inner product space: W⊥ consists of all vectors orthogonal to every w in W, with examples and basis construction.
Explore the adjoint of a linear operator with respect to inner products, its matrix representation, and conditions for self-adjointness, including the spectral theorem and eigenvector bases.
Explore unitary operators that preserve inner products and norms, and understand isometries as norm-preserving mappings, including plane reflections as examples and conditions for linearity.
Explain the structure of normal operators and their spectral properties. Show that finite-dimensional normal operators are unitarily diagonalizable, with eigenvalues and eigenvectors, and that commuting products preserve normality.
Explore the Gram-Schmidt process for orthogonalisation of quadratic forms, constructing orthonormal bases via projections under a defined inner product.
Are you interested in learning more about linear algebra?
Linear algebra is central to almost all areas of mathematics. For instance, linear algebra is fundamental in modern presentations of geometry, including for defining basic objects such as lines, planes, and rotations. Also, functional analysis, a branch of mathematical analysis, may be viewed as the application of linear algebra to spaces of functions.
If this sounds interesting to you then I encourage you to check out my blog or consider signing up for one of my classes on Udemy!
This course gives you a broad overview of several concepts and practice problems of Algebra. If you want to learn Algebra concepts from scratch and become an Algebra master, this course is for you!
If you're looking to gain a solid foundation in Linear Algebra, allowing you to study on your own schedule at a fraction of the cost it would take at a traditional university, to further your career goals, this online course is for you
The course aims to introduce
Real n-dimensional vector spaces,
Abstract vector spaces and their axioms,
Linear Transformations,
Inner Product,
Orthogonality,
Cross product and their geometric applications,
Subspaces,
Lines Independence,
Bases for Vector Spaces,
Dimension,
Matrix Rank,
Eigen Vectors,
Eigen Values,
Matrix Diagonalization