Rakesh Nagda (rakeshn@seas.upenn.edu)

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

- 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)

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

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.

- Online lessons will be available every week on the CANVAS account for the class.
- 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 (see under CANVAS account).
- 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.

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)

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).

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.

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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