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 exams:**Two in-class midterms on Friday, February 1 and Monday, February 25; open book/notes.

**Final exam:**Wednesday, March 20, 11:30 am–2:30 pm; location: TBA; open book/notes.

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

MWF 9:00–9:50 am

Mandeville B210

F 2:00–2:50 pm

Center Hall 212

Instructor: Young-Han Kim (Office hours: MW 10–10:50 am, or by appointment; Atkinson Hall 4103)

TA: Alankrita Bhatt (Office hours: Tu 1–2 pm, Jacobs Hall 5101E)

TA: Shouvik Ganguly (Office hours: M 12–1 pm, Jacobs Hall 5101B)

TA: J. Jon Ryu (Office hours: Th 9–10 am, Jacobs Hall 4506)

TA: Pinar Sen (Office hours: W 3–4 pm, Jacobs Hall 4506)