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Fall
2021 Computer
Science Research Seminar |
General Information
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Instructor: Professor Tina Eliassi-Rad, Khoury College of Computer Sciences
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Lectures: Thursdays,
2:50 PM – 3:55 PM, Ryder Hall 293
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Office hours: Mondays
& Thursdays, 4:45 PM – 5:30 PM, Online via Zoom
o
Also, available by
appointment. Email eliassi [at] ccs [dot] neu [dot] edu to setup appointment; begin the subject line with [fa21 cs4950].
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Course website on
Canvas: https://northeastern.instructure.com/courses/86658
Overview
This one-credit undergraduate seminar provides an overview of research in machine learning and its impact on our society. Students will read and present recent scientific literature and write annotated bibliographies.
Format
Students will be divided into teams of
three. Each week, one team will present the week's reading to the class. This
involves preparing slides (in either PowerPoint, Keynote, or Google Slides) and
uploading the PDF of the slides into Canvas. Each
member of the other teams will prepare his/her own annotated bibliography
(which must be turned in before class in PDF format into Canvas). The team
giving the presentation is not required to prepare an annotated bibliography.
Prerequisites
CS 3950: Introduction to Computer Science Research.
Grading
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Attendance
and participation: 40%
· Annotated bibliographies for weekly paper reviews: 30%
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Weekly paper presentations: 30%
Textbooks
This course does not have a designated
textbook. The readings are assigned in the syllabus (see below). You are
expected to have read the assigned material before each lecture.
Schedule (Evolving
and Subject to Change)
|
# |
Date |
Topic |
Readings & Notes |
Presenter |
|
1 |
Sep 9 |
Overview & Logistics |
|
Tina
Eliassi-Rad |
|
2 |
Sep 16 |
The Big Picture |
Michael I. Jordan, Tom M. Mitchell. Machine Learning: Trends, Perspectives, and Prospects, Science, 349(6245): 255-260, 2015. David M. Blei, Padhraic Smyth. Science and Data Science, Proceedings of the National Academy of Sciences, 114(33): 8689-8692, 2017. Optional: Tom M. Mitchell. The Discipline of Machine Learning. Technical Report CMU-ML-06-108, Carnegie Mellon University, Pittsburgh, PA, July 2006. |
Team 1 |
|
3 |
Sep 23 |
The Two Cultures |
Leo Breiman. Statistical Modeling: The Two Cultures, Statistical Science, 16(3): 199-215, 2001. Jake Hofman, Duncan Watts, Susan Athey, et al. Integrating Explanation and Prediction in Computational Social Science. Nature 595, 181-188, 2021. |
Team 2 |
|
4 |
Sep 30 |
Algorithmically Infused Societies |
David Lazer, Eszter Hargittai, Deen Freelon, et al. Meaningful Measures of Human Society in the Twenty-first Century. Nature 595, 189-196, 2021. Claudia Wagner, Markus Strohmaier, Alexandra Olteanu, et al. Measuring Algorithmically Infused Societies. Nature 595, 197-204, 2021. |
Team 3 |
|
5 |
Oct 7 |
Gender Shades |
Joy Buolamwini, Timnit Gebru. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. In Proceedings of the 2018 Conference on Fairness, Accountability and Transparency (FAT’18), 2018: 77-91. |
Team
4 |
|
6 |
Oct 14 |
Stochastic Parrots |
Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret
Shmitchell. On the Dangers of
Stochastic Parrots: Can Language Models Be Too Big? 🦜
In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and
Transparency (FAccT’21), 2021: 610–623. |
Tina Eliassi-Rad |
|
7 |
Oct 21 |
Generative Adversarial Networks |
Ian Goodfellow, Jean Pouget-Abadie,
Mehdi Mirza, et al. Generative
Adversarial Networks. Communications of the ACM, 63(11): 139-144,
2020. |
Team 2 |
|
8 |
Oct 28 |
Problems with Explaining Black Box Models |
Dylan Slack, Sophie Hilgard, Emily Jia,
et al. Fooling
LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods. In Proceedings
of the 2020 AAAI/ACM Conference on AI, Ethics, and Society (AIES’20),
2020: 180-186. Optional:
Cynthia Rudin. Stop Explaining
Black Box Machine Learning Models for High Stakes Decisions. Nature
Machine Intelligence, 1: 206–215, 2019. |
Tina Eliassi-Rad |
|
9 |
Nov 4 |
Causality |
Judea Pearl. The Seven Pillars of Causal
Reasoning with Reflections on Machine Learning. Communications of the
ACM, 62(3): 54-60, 2019. |
Team 4 |
|
-- |
Nov 11 |
No class |
Veterans
Day |
|
|
10 |
Nov 18 |
Impossibility Results |
Alexandra Chouldechova. Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments. Big Data 5(2): 153-163, 2017. Jon M. Kleinberg, Sendhil Mullainathan, Manish Raghavan. Inherent Trade-Offs in the Fair Determination of Risk Scores. In Proceedings of the 8th Innovations in Theoretical Computer Science Conference (ITCS’17), 43: 1-23, 2017. Optional: Branden Fitelson. A Decision procedure for Probability Calculus with Applications. Review of Symbolic Logic 1: 111-125, 2008. Tina Eliassi-Rad, Branden Fitelson. Exploring Impossibility Results for Algorithmic Fairness Using PrSAT. Technical Report, Northeastern University, Boston, MA, March 2021. Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, et al. A Survey on Bias and Fairness in Machine Learning. ACM Computing Surveys, 54(6): 1-35, 2021. |
Tina Eliassi-Rad |
|
-- |
Nov 25 |
No class |
Thanksgiving |
|
|
11 |
Dec 2 |
Machine Learning as a Software Engineering Enterprise |
Charles Isbell. You Can’t Escape Hyperparameters and Latent Variables: Machine Learning as a Software Engineering Enterprise. NeurIPS, 2020. (The video starts at 29 minutes). Optional: Julia Bossman. Top 9 Ethical Issues in Artificial Intelligence. World Economic Forum, Oct 2016. |
N/A |