Lectures related to Vector Spaces: Basics

Covers the properties and operations of vector spaces, including addition and scalar multiplication.

Explores vector spaces, focusing on properties, examples, and subspaces within a practical exercise on polynomials.

Covers the definition and properties of vector spaces, along with examples like Euclidean spaces and matrix spaces.

Introduces abstract concepts in linear algebra, focusing on operations with vectors and matrices.

Introduces vector spaces, subspaces, linear maps, and evaluation maps, with examples and exercises for better comprehension.

Covers vector spaces, operations, and linear independence with examples from polynomials and functions.

Introduces linear combinations in vector spaces, operations, and polynomials of degree 2.

Explores polynomial operations, properties, and subspaces in vector spaces.

Covers the definition and examples of vector spaces, including subspaces and linear transformations.

Explores the definition and properties of vector subspaces in linear algebra.

Covers linear applications between vector spaces, exploring their properties and uniqueness based on bases.

Covers the definitions and properties of vector spaces, including axioms and examples.

Covers linear applications, diagonalizable matrices, eigenvectors, and orthogonal subspaces in R^n.

Explores vector space properties, operations, linear combinations, and subspaces construction.

Explores the properties and applications of the dot product in vector spaces.

Introduces linear combinations of vectors in R^n and their properties.

Introduces the concept of vector spaces and covers properties of vectorial subspaces.

Covers the representation of signals in vector spaces and inner product spaces, including the Projection Theorem.

Explores orthogonality between vectors and subspaces, demonstrating practical implications in matrix operations.

Covers orthogonality, scalar products, orthogonal bases, and vector projection in detail.