Coordinates:

Instructor:

TAs:

Office Hours:

TA/Graders:

** Welcome to CIS 515 !**

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

• Hard Margin Support Vector Machines (SVM)
• Soft Margin Support Vector Machines (SVM)
• Solving Hard Margin SVM and Soft Margin SVM using ADMM
• Linear Regression; Learning a Linear or an Affine Function
• Lasso Regression
• Solving Lasso Regression using ADMM
• Elastic Net
• Solving Elastic Net using ADMM

Syllabus (pdf)

---

Course Format

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

• Online lessons will be available every week.
• Every student is expected to listen to recorded lessons and read the corresponding material in the book before every class.
• A list of the material to be listened to and read will be available on this web page a week before the actual lecture. During lecture time, I intend to
• Take and answer questions about the material presented online for the lesson.
• Occasionally present important proofs.
• In general, attempt to motivate, demistify, and put in context the material of the lesson.
• Give an idea about applying the material to solve the homework problems.

Typically, I will not lecture during class time, although I may occasionally use some time to do this.

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)

---

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 :

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

Back to Gallier Homepage

published by:

Jean Gallier