Acknowledgements

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This course has benefited from the contributions of many talented individuals over the years.

Course Development

Dr. Mario Geiger was invaluable in developing the first version of the symm4ml codebase. Hannah Lawrence and Mario Geiger developed the decomposition algorithm that underpins much of the course content.

Backend and Grading

Backend grading functionality was developed by Tyler "Ty" Allen, Tuong Phung, and Mark Jabbour.

Content Development

Additional content was created by Dr. Behrooz Tahmasebi, Mark Jabbour, YuQing Xie, and Ameya Daigavane.

Teaching Assistants by Year

TAs make this class possible and have made it even more fun to teach. They go beyond the call of duty of office hours and grading by also providing sanity checks and preservation for what has been a very actively changing class.

  • Spring 2023: Dr. Mario Geiger, Hannah Lawrence, Tyler "Ty" Allen
  • Spring 2024: Dr. Behrooz Tahmasebi, Mark Jabbour
  • Spring 2025: Dr. Behrooz Tahmasebi, Franklin X. Wang, Anoop Sonar, Tuong Phung
  • Spring 2026: Arthur Liang, Ameya Daigavane, Hannah Lawrence

Guest Speakers

We have been fortunate to have excellent guest speakers donate their time to the course, including Dr. Jigyasa Nigam, Prof. Robin Walters, Dr. Rui Wang, Dr. Behrooz Tahmasebi, Mit Kotak, Ameya Daigavane, YuQing Xie, Hannah Lawrence, and Dr. Mario Geiger.

Project Judges

In semesters when we had projects, we were fortunate to have several Professors, Instructors, and Researchers judge our poster sessions.

Students

A big thank you to previous students whose questions, confusions, and eagle eyes have helped improve all the material and shape the course's trajectory.

CAT-SOOP

Thank you to Adam Hartz for developing CAT-SOOP, the platform that powers this course website. Thank you also to Adam, Dr. Vince Monardo, and Dr. Shen Shen for answering many CAT-SOOP questions.

Course Format

This course borrows many aspects of its format and administration from 6.390 Introduction to Machine Learning. We are grateful to the 6.390 course staff for their excellent model.

This is a non-exhaustive list of thanks.