Free Course Image Advanced Linear Algebra

Free online courseAdvanced Linear Algebra

Duration of the online course: 23 hours and 1 minutes

New course

Explore Advanced Linear Algebra with Andrews University. Discover vector spaces, transformations, and real-world applications in finance, neural networks, and more.

In this free course, learn about

  • Vector Spaces and Bases
  • Dimension, Change of Basis, and Linear Maps
  • Properties of Linear Transformations and Isomorphisms
  • Linear and Bilinear Forms
  • Inner Product Spaces and Orthogonality
  • Adjoints, Normal Matrices, and Spectral Theorems
  • Real Spectral Theorem and Positive Definiteness
  • Matrix Factorizations and Norms
  • Jordan Form and Matrix Functions
  • Applications: Markov Chains, Neural Networks, and Spectral Clustering

Course Description

Delve deep into Advanced Linear Algebra with this comprehensive online course from Andrews University, tailored to elevate your understanding of complex algebraic concepts. This course is essential for anyone seeking to expand their knowledge in algebra and related fields, incorporating a detailed exploration of vector spaces, transformations, and matrix functions.

Discover the intricacies of vector spaces, exploring spans, bases, and dimensions that are fundamental to building a solid algebraic foundation. Progress to change of basis and delve into the mechanics of linear transformations and their properties. Engage with advanced topics such as the half derivative and explore the concept of isomorphic vector spaces.

The course further investigates linear and bilinear forms, touching on the vital concepts of inner products, norms, and orthogonality. Master techniques like the Gram-Schmidt process and understand the importance of an orthogonal basis in solving complex algebraic problems.

Advanced learners can challenge themselves with units on the adjoint of linear transformations and Schur's unitary triangularization. The curriculum extends to normal matrices and the real spectral theorem, providing insights into positive definite matrices and their applications.

Explore polar and singular value decompositions, followed by an analysis of Frobenius and operator norms. Take on the stimulating task of finding blocks of the Jordan decomposition and understanding the broader framework of the Jordan decomposition generalized eigenspace.

Courses on functions of matrices, including exponential and trigonometric functions, prepare students for practical applications across various disciplines. Gain an edge in mathematics and related fields with topics on tensors, Markov chains in finance, epidemiology, and page ranking. The inclusion of linear algebra in neural networks and spectral clustering emphasizes real-world applications, bridging theoretical concepts with practical solutions.

Course content

  • Video class: Advanced Linear Algebra 1: Vector Spaces 41m
  • Exercise: What is a critical assumption when checking if a subset of a vector space is also a vector space?
  • Video class: Advanced Linear Algebra 2: Spans 46m
  • Exercise: What concept ensures polynomials are a vector space?
  • Video class: Advanced Linear Algebra 3: Bases 47m
  • Exercise: What is the standard basis for R3?
  • Video class: Advanced Linear Algebra 4: Dimension of a Vector Space 48m
  • Exercise: What determines linear independence of vectors in R4?
  • Video class: Advanced Linear Algebra 5: Change of Basis 48m
  • Exercise: Matrix Transformation in Polynomial Vector Spaces
  • Video class: Advanced Linear Algebra 6: Linear Transformations 40m
  • Exercise: What is the underlying concept of linear algebra emphasized in the video?
  • Video class: Advanced Linear Algebra 7: Properties of Linear Transformations 41m
  • Exercise: What is the result of a linear transformation on polynomials of degree up to 3 when applying the derivative twice?
  • Video class: Advanced Linear Algebra 8: The Half Derivative 42m
  • Exercise: What is the half derivative of sine of x?
  • Video class: Advanced Linear Algebra 9: Isomorphic Vector Spaces 45m
  • Exercise: Which vector spaces are isomorphic?
  • Video class: Advanced Linear Algebra 10: Linear Forms 48m
  • Exercise: Identify the Dot Product Example
  • Video class: Advanced Linear Algebra 11: Bilinear Forms 50m
  • Exercise: Properties of Bilinear Forms
  • Video class: Advanced Linear Algebra 12: The Inner Product 48m
  • Exercise: What are the properties of the dot product in linear algebra?
  • Video class: Advanced Linear Algebra 13: Norm, Triangle Inequality, Orthogonality 48m
  • Exercise: What is the main focus of an inner product space?
  • Video class: Advanced Linear Algebra 14: Orthogonal Basis 47m
  • Exercise: What is an Orthonormal Basis?
  • Video class: Advanced Linear Algebra 15: Gram-Schmidt 48m
  • Exercise: What is the main goal of the Gram-Schmidt process?
  • Video class: Advanced Linear Algebra 16: Adjoint of Linear Transformation 47m
  • Exercise: What is the concept of the transpose of a matrix associated with in linear algebra?
  • Video class: Advanced Linear Algebra 17: Schur's Unitary Triangularization 44m
  • Exercise: What describes the form of the matrix A after the linear transformation involving eigenvalues?
  • Video class: Advanced Linear Algebra 18: Normal Matrices 49m
  • Exercise: Which condition must a complex matrix satisfy to be diagonalizable with a unitary matrix?
  • Video class: Advanced Linear Algebra 19: Real Spectral Theorem 43m
  • Exercise: How can you ensure real decompositions from a real matrix?
  • Video class: Advanced Linear Algebra 20: Positive Definite 46m
  • Exercise: What conditions must a matrix satisfy to be used in defining an inner product over complex numbers?
  • Video class: Advanced Linear Algebra 21: Polar Decomposition 49m
  • Exercise: What is a Positive Semi-Definite Matrix Equivalent to?
  • Video class: Advanced Linear Algebra 22: Singular Value Decomposition 46m
  • Exercise: What is the function of the Singular Value Decomposition (SVD) in linear algebra?
  • Video class: Advanced Linear Algebra 23: Frobenius and Operator Norm for Matrices 49m
  • Exercise: Determining the operator norm of a matrix
  • Video class: Advanced Linear Algebra 24: Finding Blocks of Jordan Decomposition 45m
  • Exercise: When is a matrix diagonalizable?
  • Video class: Advanced Linear Algebra 25: Jordan Decomposition Generalized Eigenspace 47m
  • Exercise: What is a key characteristic of matrices with the same Jordan form?
  • Video class: Advanced Linear Algebra 26: Functions of Matrices (Exponential, Trig, etc.) 47m
  • Exercise: What is the correct definition of e to the power of a matrix A (e^A)?
  • Video class: Tensors In Mathematics, Physics, and Computer Science 45m
  • Exercise: What is the main concept discussed in the video about?
  • Video class: Markov Chains and their Applications to Finance, Epidemiology, and Page Rank 42m
  • Exercise: Understanding Markov Chains
  • Video class: Linear Algebra and Neural Networks 45m
  • Exercise: What is the primary purpose of a perceptron in a neural network?
  • Video class: Spectral Clustering 37m
  • Exercise: What is the main purpose of spectral clustering?

This free course includes:

23 hours and 1 minutes of online video course

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