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Related lectures (21)
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Convex Optimization: Notation and Matrix Norms
Introduces Convex Optimization notation, convex functions, vector norms, and matrix properties.
Singular Value Decomposition: Fundamentals and Applications
Explores the fundamentals of Singular Value Decomposition, including orthonormal bases and practical applications.
Linear Algebra Basics
Covers fundamental concepts in linear algebra, including linear equations, matrix operations, determinants, and vector spaces.
Singular Value Decomposition
Covers the Singular Value Decomposition (SVD) of a matrix and its applications.
Vectors: Fundamentals
Covers the basic concepts related to vectors, including their definition, operations, and properties, as well as applications through examples and the Varignon's theorem.
Linear Algebra: Matrices and Linear Applications
Covers matrices, linear applications, vector spaces, and bijective functions.
Singular Value Decomposition: Orthogonal Vectors and Matrix Decomposition
Explains Singular Value Decomposition, focusing on orthogonal vectors and matrix decomposition.
Vectors: Definitions and Operations
Introduces vector definitions, displacement, addition, and applications in geometry.
Vector and matrix operations
Covers basic vector operations in MATLAB and Octave, including manipulation techniques and operations.
Orthogonal Projection Theorems
Covers the theorems related to orthogonal projection and orthonormal bases.
