This course is running at Penn during the Fall of 2023. This course is structured around recorded video lectures that students are expected to watch on Mondays followed by discussion sessions on Wednesdays and Fridays. Most video lectures last between 40 and 60 minutes, although some are a little longer. Follow this link to access lecture recordings and materials.
If you haven’t already realized this, please be aware that learning from a video is tough. This is why we have the discussion sessions but I also want you to make the most of the recorded lectures. To that end, you will see that individual lectures have been separated into modules that last between 3 and 10 minutes and that modules are aggregated in a playlist. This is intended for you to look at them separately or all together. Most people find that watching videos in short chunks is best.
Whatever your watching preferences, be aware that it is very easy to watch a video and not get anything out of it. To help you with that, in addition to recordings, you will see that each lecture comes with a handout and a script. If you put together the handout and the script, that’s the lecture for you. My idea is that you will be able to download the handout and the script and have them by your side as you watch the lecture. I am a big believer in associating space and concepts. I am hoping that the printed handout and script help you with that
We meet on Wednesdays and Fridays at 8:30 am and 10:15 am Eastern Time in the active learning classroom on the 4th floor of Walnut 3401. These meetings are reserved for interactions. Come prepared to talk. Be ready to ask questions. Be ready to answer questions.
Please reserve 90 minutes of your time for these meetings. That is, reserve Wednesdays and Fridays from 8:30 am to 10 am or 10:15 am to 11:45 am Eastern Time, depending on your section. However, we will try to keep the discussion sessions to 60-70 minutes instead of 90.
On the same Monday when we start work on a set of lectures, we also start work on a set of questions associated with each lecture. You will be randomly assigned to groups of size 3-4 each, which will be announced at the beginning of the semester. Each group is required to prepare 1-3 slide(s) with their response to their assigned questions (group<->question assignments to be announced before the beginning of each week), as well as a speech lasting about 5 minutes. During the interactive sessions, you will share the slide(s) and speech with the rest of the class, and the instructors will provide feedback and open the floor for discussion. Half of the questions will be discussed on Wednesdays, and the other half will be discussed on Fridays. You can prepare your response using whatever software you choose. If you are a latex person, you can download a Penn-branded template.
The responses you will give during the interactive session are zero-stakes. Do your best and don’t stress. You are free to remember that I failed Programming 101 and Electronics 101 when I was in college.
On the Mondays of Weeks 2, 4, 6, 8, and 11, we will release Lab assignments. These assignments will be due on the Fridays of Weeks 3, 5, 7, 10, and 12. This means you have two work weeks to complete each assignment except for the one we will release on Week 8, which you will have to complete in 3 weeks. The grades for each lab will be announced on the Monday following the lab due date on Friday (i.e., on the same day the next lab is announced). You will then have two days (until the end of Wednesday) to submit corrections if some of your answers are incorrect. If you miss the original due date on Friday, you may make a late submission until the following Wednesday and receive partial credit, without a chance for resubmission. More details on the grading criteria can be found below.
You will work in groups of three. We will send information about pairings, and you will keep these pairings throughout the term. The labs are designed to be worked on your own. The purpose of having teams is to give you a sounding board. If you are stuck on a question, chances are your partners are not. If the three of you are stuck on a questions, it may be a good idea to submit a question to the discussion board for the TAs to answer.
Each of your lab groups is paired with an individual TA. This is your main point of reference when you need help. The TAs are also going to arrange two two-hour blocks per week where you can meet with them. Take advantage of these meetings. You don’t know your TAs yet, but I do. I can vouch that they are knowledgeable and hard working. They are also good people and will help you out as much as you need.
When you complete the assignment, you will have to submit a report for the group by having one group member submitting the report to Canvas. The labs will contain very concrete instructions on how to write the reports. I am interpreting the lab reports under the language of the Paperwork Reduction Act. They are intended for you to complete in less than one hour after you finish your work. No need for florid language, showing work, or code submission. For each question we will ask for a short deliverable. The goal is simply to check that your answers are correct. If you answers are note correct, we will get back to you to offer you a chance for corrections. As long as you implement these corrections, you will get full marks for the assignment if your grade is greater than or equal to 14, and partial marks if your grade is below 14. The link for the Canvas page can be found here.
The labs are designed to illustrate points I will be making in lectures. For example, Lab 1 will be about understanding the importance of choosing appropriate parametrizations when we are we solve a problem using learning techniques. I am telling you this because labs also have a heavy development component. You will have to write some code that is not difficult but not easy either. I don’t want you to loose focus on the fact that the objective of the lab is not to get the code working. Rather, the focus of the lab is to understand why your code works.
The labs will use the Python programming language, the PyTorch library for machine learning, and the Alelab Graph Neural Network library. In Lab 3 we are going to explain how to use the Alelab GNN library. However, we will assume from the start that you are familiar with Python and that you have succeeded at installing Pytorch. You don’t need to be familiar with Pytorch. We will go over the use of this library in Labs 1 and 2. For those of you that are not familiar with Python, and need to install Python and Pytorch, please refer to this guide that we have put together.
We are running a discussion Forum on Ed Discussion. Please be engaged. Submit questions. Answer questions. Offer comments. Offer help
Juan Elenter (firstname.lastname@example.org) I’m a third-year PhD. student at the University of Pennsylvania, where I mostly think about optimization theory, signal processing and machine learning with Prof. Ribeiro. Before graduate school, I spent some time at Stanford’s Kundaje Lab, working on chromatin accessibility prediction and genomic motif discovery. I was also a summer intern at CERN, contributing to the CMS Open Data initiative with Dr. Lassila-Perini. I’m originally from Montevideo, Uruguay, where I obtained a BSc. in Electrical Engineering from UdelaR. During my undergrad I was a software developer at IBM.
Ignacio Hounie (email@example.com) is a third-year PhD. student at the University of Pennsylvania, advised by Prof. Alejandro Ribeiro. He is broadly interested in machine learning, statistical signal processing and optimization. He is originally from Montevideo, Uruguay, where he received his BSc. in Electrical Engineering from UdelaR in 2020.
Shervin Khalafi (firstname.lastname@example.org) is a second-year PhD student at the University of Pennsylvania advised by Prof. Ribeiro. Currently, he is working on the theory of graph neural networks and their optimization. More broadly, his research interests include Optimization, GNNs and Learning theory. He is originally from Tehran, Iran, where he received a BSc. in Electrical Engineering from Sharif University of Technology.
Pavlos Kallinikidis (email@example.com) Pavlos Kallinikidis is a second-year PhD student at the University of Pennsylvania, advised by Prof. George Pappas. His interests lie at the intersection of Machine Learning, Dynamical Systems and Scientific Computing. He obtained his BSc. in Electrical and Computer Engineering from Aristotle University of Thessaloniki, Greece.
Jiashu He (firstname.lastname@example.org) I am a second-year Ph.D. in CIS at the University of Pennsylvania, working on Graph Neural Networks (GNN), especially utilizing GNN to solve problems in the domain of graph theory and natural languages, where I am fortunate to be advised by Professor Alejandro Ribeiro.
There are 5 labs in this course, and each of them counts for 16 points. If all of the answers you submit are correct, you get 16 points for the lab. If some of your answers are incorrect, you get a chance to submit corrections. After submitting your corrections, we will grade your resubmission and give you a maximum of 14 points for the lab. In case you skip the main due date and make a late submission (by the end of the following Wednesday), we will grade your late submission and give you a maximum of 12 points. No resubmissions are allowed in this case. The remaining 20 points will come from your participation during the weekly meetings. You will also be able to get 10 extra points for paper presentations.
This grading rubric is designed so that if you attend all meetings and submit all of your assignments on time and also submit the corrections, you get an A- for the course. You will get an A for the course if at least 3 of your original reports are correct on the first submission and you attend and actively participate in the meetings. Your grade could go downhill quite quickly if you mostly submit your assignments late and/or do not submit the assignments at all.
Some of you are not getting credit for this course. We are inspired by your willingness to learn, but please keep us inspired. We just ask that you take the class as seriously as students that are officially registered. That is, watch the videos, show up to the interactive sessions, and complete the assignments.
The last component of the course is Paper Presentations, which can highlight the connections between the course and the state-of-the-art in the graph neural networks literature. You can find a list of suggested research papers that are related to the materials we cover in the course. If you are interested in any of these papers, you can volunteer to have a 10-15 minute presentation about it during our interactive meetings. This is not an exhaustive list, and you could also consider any other relevant papers in the literature, in which case you will need to first share it with the instructor and get my approval before presenting it to the class. The presenting students will receive 5 extra points for each presented paper.
Please reserve a spot if you would like to present a paper to the class during any of the upcoming interactive meetings. Also, contact me (email@example.com) and make sure to give at least a 48-hour advance notice.
Charilaos I. Kanatsoulis (firstname.lastname@example.org) is a postdoctoral researcher in the Department of Electrical and Systems Engineering at the University of Pennsylvania. He received his Diploma (B.S. and M.E. degree) in electrical and computer engineering from the National Technical University of Athens, Greece, in 2014, and his Ph.D. degree in electrical and computer engineering from the University of Minnesota, Twin Cities, in 2020. His research interests lie in the intersection between machine learning and signal processing and include convolutional and graph neural network design, tensor analysis, representation learning and explainable artificial intelligence.
Alejandro Ribeiro (email@example.com) : I am an older version of the guy on the left and a Professor of Electrical and Systems Engineering (ESE) at the University of Pennsylvania (Penn). I was born in Montevideo, Uruguay where I studied and got an Electrical Engineering degree from the Universidad de la República in 1999. I worked as a systems engineer for a cellphone company until 2003 when I moved to the United States to study at the University of Minnesota (UoM) in Minneapolis. I received M. Sc. and Ph. D. degrees from the UoM in 2005 and 2008, respectively.
In case you need either, you can access my professional bio in this link and my academic cv in this link. These two documents are prepared by the serious person I am forced to impersonate every so often. To get a sense of my research and teaching activities please keep reading. For a more comprehensive discussion visit my lab’s website which has descriptions of my research vision and provides access to the sites for the courses I teach. I am hoping that you will find my lab’s blog to be illuminating and entertaining but if you are hard core, my list of papers is here.
I have had and continue to have the honor of working with remarkable doctoral students. Their papers have received a number of awards which I am happy to claim as my own. These awards are the 2020 Signal Processing Society young author best paper award, the 2014 O. Hugo Schuck best paper award and paper awards at the 2020 International Conference on Acoustics, Speech and Signal Processing, the 2019 European Signal Processing Conference, the 2017 Conference on Decision and Control, the 2016 Workshop on Statistical Signal Processing, the 2016 Sensor Array, and Multichannel Signal Processing Workshop, the 2015 Asilomar Conference on Signals Systems and computers, the 2013 American Control Conference, and the 2005 and 2006 International Conferences on Acoustics, Speech, and Signal Processing. I also have had and continue to have the honor of working with remarkable undergraduate students. I am very proud of their recognition in the form of the 2012 S. Reid Warren, Jr. Award presented by the undergraduate student body of Penn Engineering for outstanding teaching as well as the recognition of my colleagues with the Penn-wide 2017 Lindback award for distinguished teaching. I have also been selected as a Fulbright Scholar class of 2003 and a Penn Fellow class of 2015.