CIS 5150, Spring 2024
Fundamentals of Linear Algebra and Optimization

Course Information
January 21, 2024

Coordinates:

Tuesday-Thursday, 12pm-1:29pm, FAGN 216

Instructor:

Jean H. Gallier, Levine 476, 8-4405, jean@seas.upenn.edu

TAs:

Jocelyn Quaintance (jocelynq@seas.upenn.edu)
Dominic Olaguera-Delogu (delogu@seas.upenn.edu)
Adwait Agashe (aadwait@seas.upenn.edu)
Muskaan Berival (musk@seas.upenn.edu)

Office Hours:

Jean: zoom link and time TBA

TA/Graders:

Jocelyn Quaintance (jocelynq@seas.upenn.edu)
Dominic Olaguera-Delogu (delogu@seas.upenn.edu)
Adwait Agashe (aadwait@seas.upenn.edu)
Muskaan Berival (musk@seas.upenn.edu)


** Welcome to CIS 5150 !**

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 more material this Spring 2024 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

In order to increase the level of interation between the students and the instructor(s) I propose to use the following scenario.


Classes will be recorded and 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 but past experience showed that it is difficuilt too implement so I will most likely 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: BAN_CIS-5150-001 202410 (course number 1772458)
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.

In addition to the recorded lessons in "Class Recordings" of Canvas, there are slides corresponding to these lessons in the "Files" Section of Canvas.

Make sure you look at these slides because some of them do not exist as recorded lessons.

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

In preparation for this week, please watch the videos in Class Recordings, Module 1,
and read the files in Files specified under Content (In Module 1).
Also read Pages 29-69 of the slides https://www.cis.upenn.edu/~cis5150/cis515-20-sl1-a.pdf.
Details and proofs are given on Page 29-61 of https://www.cis.upenn.edu/~cis5150/linalg-I-f.pdf
You should skim Section 2.3, but ignore the details.


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:

html


[   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!

Back to Gallier Homepage

published by:

Jean Gallier