Past offerings:

Instructor: Da Kuang

Course description:
This course introduces numerical linear algebra from a data analysis perspective. Emphasis will be given to matrix computation arising from unsupervised clustering, dimension reduction, and optimization. In the first half of the course, students will work on mini-projects that relate numerical linear algebra to data analysis tasks; in the second half, students will read and implement a recent research paper on large-scale machine learning involving matrix computations. Overall, this course offers a solid understanding of the theory and practical implementation of matrix algorithms, which is important for effectively using existing machine learning tools and packages as well as creating new ones.







Homework (tentative)

Each homework assignment includes 1~2 theoretical questions and a mini-project.
Please note that while discussion is allowed and encouraged, individual students must write up their own answers and computer programs.
All the students must observe the conduct code.

Final Project

Project Instructions

In the final project, you will:

Each group should communicate with the instructor about the scope of experiments and the deliverables. You will very likely need a little literature search in order to understand the algorithms.

Example paper list: (for papers considered for implementation for the current quarter, please refer to the "Paper walk-through" slides on CCLE)


Date Mon Wed Fri
Jan 9
Jan 11
Jan 13
Introduction Topics in Python Numerical software stack
HW1 out
Jan 16
Jan 18
Jan 20
(MLK holiday) Matrix-vector multiplication Vector and matrix norms
Singular value decomposition
Jan 23
Jan 25
Jan 27
Singular value decomposition (cont'd) Low-rank approximation
HW1 due; HW2 out
Principal component analysis
Jan 30
Feb 1
Feb 3
Spectral clustering Spectral clustering
HW2 due; HW3 out
Team formation due
Sparse matrices
Feb 6
Feb 8
Feb 10
Project overview Collaborative filtering
HW3 due; HW4 out
Sparse coding
Feb 13
Feb 15
Feb 17
Image classification pipeline Conditioning of a matrix
HW4 due
Feb 20
Feb 22
Feb 24
(President's Day holiday) Projectors
Least squares algorithms
Project proposal due
Techniques for introducing zeros
for dense LU, QR, SVD/EVD
Feb 27
Mar 1
Mar 3
Locally linear embedding Locally linear embedding Nonnegative matrix factorization
Mar 6
Mar 8
Mar 10
Iterative solvers and eigensolvers Iterative solvers and eigensolvers Review session
Mar 13
Mar 15
Mar 17
Final presentations Final presentations Final presentations

Final exam: Mar 21 (Tuesday), 8:00-11:00am

Final report due: Mar 24 (Friday) 11:59pm