21-670 Linear algebra for data science
Instructor: Zecheng Zhang.
Office: Wean Hall 7216.
Office hour: 1:30 - 3:30 pm Tuesday. I may change my office hours but I will let you know. You can also email me to make an appointment.
The syllabus (tentative) is available here.
I will post the homewrok in Gradescope in Canvas.
I will upload the class notes in this section.
Week 1, Aug 28 (Mon). Part 1: matrix, EROs, matrix multiplication, LU.
subspace, col/null/row space, dimension, rank, rank theorem, CR factorization, rank inequalities.
Week 2, Sept 7 (Wed). Part 1: eigenvalue, similarity, diagonalization, inner product.
Part 2: orthogonality, orthogonal projection, orthogonal matrix, properties of orthogonal matirx.
Part 3: preview, done: SVD construction, rank and singular values.
Week 3, Sept 12 (Mon). Part 1: preview,
done: range(A), row(A) and SVD. Part 2: Full SVD, and examples. Part 3: symmetric matrices, spectral theorems, A^tA and SVD.
Week 4, Sept 19 (Mon). Preview (updated). Part1: L2 norm, rank k approximation for L2. Part 2: Courant Fisher. Part 3: Weyl's inequality, best approximation theorem for F norm.
week 5, Sept 26 (Mon). Part 1: POD. Part 2: preview. Done positve definite, LU of PD, Cholesky factorization, quadratic form.
week 6, Oct 3 (Mon). Part 1: check notes of the last time. Part 2: preview.
week 7, Oct 13. Preview (updated) .