Learning and Optimization for Signal
|Mondays and Wednesdays 10:10-11:25 AM
Prof. John Wright
Office: 716 CEPSR
Office hours: Thursdays 10-11 AM
(or by appointment)
Office hours: TBA
The goal of this course
is to introduce
students to tools from numerical optimization and statistical learning
that are useful for intelligently processing signals such as images,
videos, audio and more. The course will be taught at at the level
undergraduate or master's student, with an emphasis on problem
and Lecture Notes:
- January 23 -- What is it all about?
introduction, examples, review of linear algebra
HW0 released, due January 30 (just a survey!)
Lecture 1 notes are available on CourseWorks.
- January 28 -- More linear algebra review, eigenvectors of
We reviewed linear
transformations, matrices, systems of equations
We showed that symmetric matrices have real
eigenvalues and a corresponding orthonormal basis of eigenvectors.
Some fun reading on PageRank and related topics: The
$25,000,000,000 Eigenvector: The Linear Algebra Behind Google
- January 30 -- The singular value decomposition
The material from
Lectures 2-3 can also be found in the Lecture 1 notes on CourseWorks.
- February 4 -- Optimality and Computation of SVD, examples
Turk and Pentland, Eigenfaces for
Basri and Jacobs, Lambertian
Reflectance and Linear Subspaces
Deerwester, Dumais, Furnas, Landauer,
by Latent Semantic Analysis
Latent Semantic Analysis
Lecture 5 - February 6 -- More
SVD examples, nonlinear extensions
- due January 30 in class [pdf] [tex]
A quick survey to help us learn
more about your background and goals for the course.
If you had
trouble downloading the file, you can turn this in February 4, or email
it to me.
- due February 25, available on CourseWorks
linear algebra (essential) and probability (desirable). Having already
taken signals and
systems would be preferable. The course is aimed at undergraduates and
MS students. PhD students working in other areas are also welcome. If
you're uncertain whether to take the class, please talk to me.
There are no required
texts. The instructor's lecture notes will be provided on CourseWorks.
Students will be encouraged to augment
reading with material from:
Tibshirani + Freedman, The
Elements of Statistical Learning
there will be
homework, a midterm exam (date TBA) and a final project.
analytical work and Matlab experiments.
will be mostly
analytical. I will provide a list of topics and some example questions
closer to the time.
complete independent course projects. Have fun and be creative in
formulating your project! Virtually any idea is ok, as long as it is
relevant to the course material and well-executed. A great course
project might involve an application of the tools learned in the course
to a new real-life problem, or a deeper study of some aspect of the
course material. If
you have questions or potential topic ideas, please come to office
hours. I will be happy to discuss with you.
Most of the assigned
homework will be given during the first 3/4 of the semester to allow
ample time for the final project.
Do your own work!
of Engineering FAQ on Academic
assignments with other students is
permissible, but work out the answers on your own. Copying work is impermissible
and will be dealt
with harshly if detected.