CIS 515, Fall 2020
Fundamentals of Linear Algebra and Optimization

Course Information
December 6 2020


Monday-Wednesday, noon-1:30pm


Jean H. Gallier, Levine 476, 8-4405,


Anton Xue (
Rakesh Nagda (

Office Hours:

Jean: zoom link and time TBA


** Welcome to CIS 515 !**
I have provided a list of dates for HW/projects
The zoom link is the same as before.

Since a lot of material for the fully online version of this course, MCIT 515, is available online, I plan to make use of this material, supplemented by extra slides. Consequently, I plan to cover substantially more material this Fall 2020 than I used to cover in the past. In particular, I will cover some elements of optimization theory (the Lagrangian framework, ADMM) and some topics from machine learning, including

Syllabus (pdf)

Link to Workshop on Equivariance and Data Augmentation, September 4, 2020 (html)

Course Format

This course will be fully taught online. In order to increase the level of interation between the students and the instructor(s) I propose to use the following scenario.

Typically, I will not lecture during class time, although I may occasionally use
some time to do this.
Classes will be recorded an uploaded to CANVAS.

Consequently, there will be a heavier burden and a greater requirement of self-discipline placed on the student to listen to and read the lessons to keep up with the course.

On the other, you will have greater flexibility in deciding when to listen and read the lessons in preparation for the actual class, which I hope, will be more of an interactive class.

We will try this learning mode. If it does not work we will switch back to a more traditional lecturing mode.

There will be no midtems, no final exam, but instead homework problems (some challenging) and (Matlab) projects (about seven)

CANVAS Account

There is a CANVAS account for the course: CIS 515-001 2020C
You should have access to it using your Pennkey.

This account contains the video recordings and reading material that
you should consult each week prior to class (a zoom link will be provided).

Look for Class Recordings and Files.

Unless specified otherwise, a Module corresponds to two lectures (one week's worth).

For this week, please watch the videos in Class Recordings, Module 14.
Also take a look at Chapter 19 in our book (Vol II).

Textbook: The official textbooks are Linear Algebra and Optimization with Applications to Machine Learning, Vol I and Vol II, by Gallier and Quaintance, World Scientific (2020). Relevant Chapters will be available as needed; see Slides and Notes

Latex Help:


[   Grade (Homeworks, Projects)   |  Additional Resources   |  Slides and Notes   ]

A Word of Advice :

Expect to be held to high standards, and conversely! In addition to slides, I will post lecture notes. Please, read the course notes regularly, and start working early on the problems sets. They will be hard! Take pride in your work. Be clear, rigorous, neat, and concise. Preferably, use a good text processor, such as LATEX, to write up your solutions.

Due to the difficulty of the homework problems and in order to give you an opportunity to learn how to collaborate more effectively (I do not mean "copy"), I will allow you to work in small groups. A group consists of AT MOST THREE students.

You are allowed to collaborate with the same person(s) an unrestricted number of times.
Only one homework submission per group. All members of a group will get the SAME grade on a homework or a project (please, list all names in a group).

It is forbidden to use solutions of problems posted on the internet. If you use resources other than the textbook (or the recommended textbooks) or the class notes, you must cite these references.

Plagiarism Policy

I assume that you are all responsible adults.
Copying old solutions verbatim or blatantly isomorphic solutions are easily detectable.
DO NOT copy solutions from old solution sheets, from books, from solutions posted on the internet, or from friend!
Either credit will be split among the perpetrators, or worse!

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Jean Gallier