The matrix is everywhere. — Morpheus

Mathematical foundation of linear algebraic techniques and their applications in signal processing, communication, and machine learning. Geometry of vector and Hilbert spaces, orthogonal projection, systems of linear equations, the role of sparsity, eigenanalysis, Hermitian matrices and variational characterization, positive semidefinite matrices, singular value decomposition, and principal component analysis.

Undergraduate linear algebra at the level of Math 18 or 20F. Working knowledge of mathematics.
**These prerequisites should be taken very seriously.**

**Weekly homework assignments:**Homework will be normally be assigned each Wednesday and due the following Wednesday in class.**Late homework will never be accepted.**If you use any material other than the textbooks and lecture notes — this applies to having discussions with classmates or searching the Internet — the source should be clearly mentioned. Relying on the course material from previous quarters is considered a violation of the honor code. Arbitrarily selected subsets of problems will be graded.

**Midterm exam:**In-class midterm on Monday, February 12; open book/notes.

**Final exam:**Monday, March 19, 11:30 am–2:30 pm; location: TBA; open book/notes.

**Grading:**Homework 30%, midterm 30%, and final 40%. We reserve the right to change the weights later.

MW 11:00 am–12:20 pm (Section A), 12:30 – 1:50 pm (Section B)

Jacobs Hall 2315

Instructor: Young-Han Kim (Office hours: M 2–3 pm, W 2–3 pm, or by appointment; Atkinson Hall 4103)

TA: Shouvik Ganguly (Office hours: Tu 4–5 pm; Jacobs Hall 4506)

TA: Alankrita Bhatt (Office hours: Th 4–5 pm; Jacobs Hall 4506)