To develop locally:
git clone https://github.com/aicolumbia/aicolumbia.com.git
cd aicolumbia.com
npm install --legacy-peer-deps
npm run dev
This project uses the MIT license.
ai@columbia is open to all Columbia University undergrads, grad students, interns, postdocs, and faculty interested or working in artificial intelligence and machine learning. No prior experience is necessary, and newcomers are welcome :)
We noticed that Columbia can be siloed, with little communication between departments, campuses, or groups of students or newcomers, so we started this group to improve things! We are now at 500+ members and host regular events across campuses and departments.
Join us! Here are the ways:
- Sign up at aicolumbia.zulipchat.com and introduce yourself in the
#introstopic! This also adds you to the mailing list. - Join our monthly happy hour! We meet on the 9th floor of the Zuckerman Institute (at the Jerome L. Greene Science Center, 3227 Broadway, New York, NY 10027 at the Manhattanville campus; https://goo.gl/maps/tBh3zVhBB9G6Gj8dA) 430-8pm on the first Monday of every month for a happy hour and speaker series.
- Email us at ai@columbia.edu with any questions, or if you have ideas for topics such as venture capital, ethics and fairness issues in machine learning and artificial intelligence, and other meetups and hackathons that are planned.
If you cannot afford to purchase these, you can often access them through a Library Genesis or Sci Hub search.
Ethics and fairness:
General artificial intelligence and machine learning:
- Cosma Shalizi, Advanced Data Analysis from an Elementary Point of View
- Hal Daume's course
- Machine Learning Mastery
- Probability and Statistics for Data Science
- The Best Statistics textbook
- Stanford NLP Course
We maintain the following list of all graphics processing units (GPUs) on campus that are accessible to students depending on their affiliation:
| Name | Link | Contact | Notes |
|---|---|---|---|
| Habanero | CUIT website | hpc-support@columbia.edu | 14 Nvidia K80 GPUs and 13 Nvidia P100 GPUs |
External GPUs:
Colab Pro notebooks (https://colab.research.google.com/) come with GPUs that have 16GB of memory. However, there is a time limit, so these are better for shorter development jobs, and experiments. The limits change depending on your usage. Sometimes they cut you out instantly; sometimes, it lasts longer than a day. If you use Colab Pro intensive, you might get "banned" (i.e., will be disconnected from GPU immediately as soon as you run the notebook) for a while.
RSVP here! https://bit.ly/ai-image-generation
Jointly organized with Floodgate Fund:
