(A) Linear algebra 1: Characteristic polynomial
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(A) Linear algebra 1: Vector spaces
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(A) Linear algebra 10: Quotient space; equivalence relation
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(A) Linear algebra 11: Determinant
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(A) Linear algebra 12: Eigenvalues
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(A) Linear algebra 14: Trace of a matrix
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(A) Linear algebra 15: Diagonalization; triangular matrices
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(A) Linear algebra 19: Convex set induces a norm
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(A) Linear algebra 2: Basis and dimension
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(A) Linear algebra 21: Lp-spaces of integrable functions
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(A) Linear algebra 22: Scalar product
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(A) Linear algebra 23: Orthogonal vectors and basis
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(A) Linear algebra 24: Orthogonal matrices
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(A) Linear algebra 25: Symmetric matrices
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(A) Linear algebra 26: Spectral theorem for symmetric matrices
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(A) Linear algebra 27: Positive definite matrices
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(A) Linear algebra 28: Variational characterization of eigenvalues
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(A) Linear Algebra 29: Singular value decomposition
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(A) Linear algebra 3: Direct sum
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(A) Linear Algebra 30: Rank-k-approximation, matrix norms
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(A) Linear Algebra 32: Continuous linear functionals and operator norm
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(A) Linear Algebra 33: Dual space, Riesz representation theorem
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(A) Linear algebra 4: Linear maps, kernel, range
Part of the Course “Mathematics for Machine Learning”, Winter Term 2020/21, Ulrike von Luxburg, University of Tübingen Source of this “Tübingen Machine Learning” AI Video
(A) Linear algebra 5: Matrices
Part of the Course “Mathematics for Machine Learning”, Winter Term 2020/21, Ulrike von Luxburg, University of Tübingen Source of this “Tübingen Machine Learning” AI Video