Linear Systems: Fundamentals and Applications

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Related lectures (30)

Page 3 of 3

Introduces the Gauss-Jordan method for solving linear equations and explores unique solutions and efficiency comparisons.

Vectorization in Python: Efficient Computation with Numpy

Covers vectorization in Python using Numpy for efficient scientific computing, emphasizing the benefits of avoiding for loops and demonstrating practical applications.

SISO Polynomial Controllers: Sylvester Matrix, Internal Model Principle

Covers the design of SISO polynomial controllers using concepts such as the Sylvester matrix and model matching.

Linear Algebra: Matrices Properties

Explores properties of 3x3 matrices with real coefficients and determinant calculation methods.

Linear Applications and Matrix Calculus

Explores linear applications, matrices, injectivity, unique solutions, and matrix operations.

Matrix Column Space Analysis

Covers the analysis of the column space of a matrix and its implications.

Linear Algebra: Matrices Operations

Covers fundamental matrix operations and their applications in solving linear equations.

Linear Algebra: Matrices and Linear Applications

Covers matrices, linear applications, vector spaces, and bijective functions.

Matrix Operations: Equivalence and Reduction

Covers the concept of equivalence of linear systems and matrix operations.

Observability and Controllability

Explores observability and controllability in linear systems, emphasizing the significance of input decoupling for observability.