Announcement: The lectures will start on Thursday, August 31, 2023. They will be held virtually for the month of September.
Lecture time: Tuesdays and Thursdays 1:45-3:15 PM
Lecture location: Virtual (on Zoom for September), DRLB 3C8
Instructor office hours: By appointment (send a private message on Ed)
Please use Ed Discussion for all course communications. You can post privately on Ed to contact me, when needed.
Waitlist information: This course require active participation from the students including role-playing style discussions, and paper presentations, and will thus be relatively small in size. If you are on the waitlist and you think the course is an ecceptionally good fit for you, please email me.
This advanced graduate-level course will focus on the latest theoretical and empirical developments in modern machine learning, with a primary focus on deep learning. We will explore cutting-edge machine learning methods such as transformers, diffusion models, self-supervised learning, and more, with an emphasis on the conceptual basis behind their design, success, and limitations. In addition to evaluating in-distribution performance, we will also assess out-of-distribution performance, robustness, emergent behavior, and optimization challenges and mysteries of these models. We will cover classical theoretical results in statistical machine learning and optimization, and explore their connections (or lack thereof) to recent empirical and theoretical research findings.
There are no official prerequisites however students are expected to have mathematical maturity and proficiency in probability, linear algebra, statistics, and fundamental machine learning concepts. Familiarity with the topics covered in CIS 5200 is recommended, but not required. While the course will primarily focus on concepts and theory, some small-scale empirical explorations may be expected, requiring a basic understanding of Python or the ability to learn it through self-study.
The following books are useful for background reading.
- Machine Learning (ML) by Tom Mitchell. Available as PDF here. A classic introduction to machine learning that assumes no knowledge of statistics or artificial intelligence.
- Elements of Statistical Learning (ESL) by Trevor Hastie, Robert Tibshirani and Jerome Friedman. Available as PDF here.
- Probabilistic Machine Learning: An Introduction (PML) by Kevin Murphy. Available as PDF here.
- Understanding Machine Learning: From Theory to Algorithms (UML) by Shai Shalev-Shwartz and Shai Ben-David. Available as PDF here. Refer to this book for a more detailed theoretical exposition of the material covered in class.
- Mathematics for Machine Learning (MML) by Marc Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. Available as PDF here.
- Linear Algebra Review and Reference by Zico Kolter. Available as PDF here.
This course will feature a combination of lectures, and student presentations, with a significant portion of class time dedicated to in-depth discussions of the presented material. There will also be a few guest lectures from leading researchers from industry and academia. The lectures will typically cover the core technical concepts. This will be followed by paper discussions involving role-playing inspired by Alec Jacobson and Colin Raffel. We will be adopting a subset of the following roles:
- Reviewer: The paper has been submitted to NeurIPS, and you have been assigned to review it. Follow the review form from NeurIPS as a guideline and submit a review for this paper.
- Archaeologist: You’re an archeologist who must determine where this paper sits in the context of previous work. You must find and report on atleast one older paper cited within the current paper that substantially influenced the current paper and atleast one newer paper that cites this current paper. Look out for follow-up work that offers criticism of the current paper.
- Student Researcher: You are looking for a new research problem and the paper piqued your interest. You have to come up with follow-up project ideas based on the paper. These can be direct improvements of the paper, or projects that use the papers results as building blocks.
- Reproducibility Checker: You want to check whether the claims of the paper are valid. You decide to create a small experiment (toy dataset, or toy model) and verify if the results hold. Make sure to check how robust they are to various choice of hyperparameters.
- Quanta Correspondent: You want to write an article on the paper for Qanta. You must explain to a broad audience the main problem the paper is addressing, why it is impotant, and what the main technical contributions are.
Paper readings will be posted a week prior to the lecture. You will be able to access them from the schedule, at least one week prior to the class. The readings marked as discussion will be presented in the above-mentioned role-play style. A sign-up sheet will be provided for discussion roles for each such reading - five students will take a subset of the above mentioned role. This role defines the lens through which you must read the paper and what you should prepare for the in-class discussion. Each student will be expected to take the ‘Reviewer’ role for two readings. Along with the review, the ‘Reviewer’ role also requires the student to present the paper to the class. For those, who are not assigned a role, you will be required to submit at least one question (clarification or discussion point) prior to the lecture. This will count towards your class participation grade.
The project can either be a literature survey or include original research:
Literature survey: You can pick any topic covered in the class or in the broader area of modern machine learning for your literature survey. Your survey must include in-depth summaries and exposition of relevant papers on the chosen topic, comparing and contrasting them.
Original research: It can be either theoretical or empirical (or a mix of the two). Make sure to get it approved by me. If you choose this option, you can do it either individually or in groups of 2. You are encouraged to use your current research in the project. The best outcome of this type of project is a manuscript that is publishable at a major machine learning conference (COLT, ICML, NeurIPS, ICLR etc.) or journal (TMLR, JMLR).
Here is the tentative timeline for the course project:
Project proposal (due on Oct 10 2022): A short document with at most 2 pages of content (plus references) (1) survey: stating the list of papers you plan to survey, and the reasons behind your choice (why you think it is important or interesting), (2) original research: describing the proposed problem, why it is interesting, and a brief list of references to prior work. Please be mindful of not being overly ambitious, and scoping the projects appropriately.
Midway progress report (due on Nov 15 2023): A document with roughly 3-4 pages of content (plus references) highlighting the progress you have made so far, and your plan for the remaining of the semester. Compared to the proposal, this should be more concrete and detailed, with preliminary results that you may have.
Final project presentations will be held in class on Dec 5 & 7 2023. Format will be decided based on the number of groups.
Final project reports (due on Dec 15 2023): This should be in the style of a NeurIPS paper. You are allowed up to 8 pages (you do not need to use all 8) of content with unlimited appendices.
Your grade will be based on three components: Discussions/Class Participation 25%; Final Project 45%. There will be no homeworks or exams.
- Discussions (55%)
- There will be 20 paper readings in total scattered in between lectures
- You will take roles in five out of the 20 paper readings
- You will need to play reviewer for two papers for 30% of the grade
- This entails presenting the paper jointly with the other reviewer (5%)
- Presenting your critique (5%)
- Submitting your written review (5%)
- May require a bit of ‘deep dig’ but not expected to read appendices unless you want to
- The other roles only require you presenting for 3 mins and you will need to do this for three papers for 15% of the grade
- At the level of ‘start to dig’
- If you are not presenting, then your participation in the discussion and questions count for the rest of the 10% participation grade (will adjust to count for participation in at least 10 out of 15 remaining papers)
- Only ‘surface level’ reading required
- (extra credit) The reproducibility role be for one paper for 10% bonus grade
- Project (45%)
- 10% for 1 page proposal
- 10% for 2-3 page progress report
- 15% for 4 page final report (with unlimited appendix)
- 10% for <=10 minute presentation
We will be reading several seminal works in machine learning as well as very recent works that have not yet been peer-reviewed. Therefore, learning how to effectively read, review, and present research papers will be an important skill for the class. Learning Theory Alliance has some very helpful resources on tips for effectively reading papers (read1 and read2) and reviewing (review1) that I encourage you to check out. The first lecture will cover some of this material.
Your mental health and wellbeing are incredibly important to me and I recognize the stresses (both school and non-school related) that many of you may be under here at Penn. I encourage you to come speak to me if you have any extenuating circumstances as you progress through this course.
I want to make sure that you are aware of some of the mental health resources here at Penn.
- Student health and counseling: the primary care and psychological care division of Wellness at Penn which can be used to schedule counseling appointments.
- Mental health at Penn: Penn’s mental health hub.
- If there is any urgent concern, please contact CAPS at 215-898-7021 for emergency help.
If you are a victim of, witness, or are otherwise affected by unacceptable behavior:
- In cases of sexual harassment and assault, please consult DPS Special Services at 215-573-3333 or 511 from a campus phone; this is a confidential resource.
- To report other bias incidents, contact the Penn Office of Diversity
- For other bias violations of the code of student conduct, the Office of Student Conduct has an bias incident reporting form
At Penn and in this class we value and actively seek to include all students and their unique identities (including but not limited to ethnicity, gender, sexual orientation, country of origin, class, religion, and disability status). You belong here, and I am here to help you. If at any time, you feel that this class is not providing an inclusive environment to your or your classmates, please let me know. I want this class, lectures, discussions, and office hours to be an inclusive space where you feel comfortable learning and sharing your opinion.
I reserve the right to make changes to the syllabus including lecture contents and project due dates. These changes will be announced as early as possible.