Starting a new Lecture Notes Series on Vectors and spaces | Linear Algebra | Khan Academy
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Vectors and spaces | Linear Algebra | Khan Academy
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Lecture 1: Defining the angle between vectors
Lecture 3: Matrix vector products
Lecture 4: Point distance to plane
Lecture 5: Adding vectors
Lecture 6: Span and linear independence example
Lecture 7: Unit vector notation
Lecture 8: More on linear independence
Lecture 10: Showing that the candidate basis does span C(A)
Lecture 11: Introduction to linear independence
Lecture 12: Visualizing a column space as a plane in R3
Lecture 13: Dimension of the column space or rank
Lecture 14: Matrices: Reduced row echelon form 3
Lecture 15: Matrices: Reduced row echelon form 1
Lecture 16: Vector triangle inequality
Lecture 17: Linear combinations and span
Lecture 19: Vector dot product and vector length
Lecture 20: Multiplying a vector by a scalar
Lecture 22: Vector intro for linear algebra
Lecture 23: Normal vector from plane equation
Lecture 24: Parametric representations of lines
Lecture 25: Vector intro for linear algebra
Lecture 26: Normal vector from plane equation
Lecture 27: Parametric representations of lines
Lecture 28: Introduction to the null space of a matrix
Lecture 29: Real coordinate spaces
Lecture 30: Matrices: Reduced row echelon form 2
Lecture 31: Cross product introduction
Lecture 32: Linear subspaces
Lecture 34: Proof of the Cauchy-Schwarz inequality
Lecture 35: Vector examples
Lecture 36: Proving vector dot product properties
Lecture 37: Column space of a matrix
Lecture 38: Dot and cross product comparison/intuition
Lecture 39: Distance between planes
Lecture 40: Null space 3: Relation to linear independence
Lecture 41: Basis of a subspace