NYU AI School

January 4 - 8, 2021

About

The NYU AI School is a week-long winter school on artificial intelligence and machine learning featuring hands-on labs and introductory lectures taught by leading experts. The school runs from January 4 - 8, 2021, and will be held entirely online.


Who should participate?
The school is primarily designed for first- and second-year undergraduates who are interested in machine learning and AI. As the school is introductory, we do not expect students to have experience with these areas. In fact, we welcome students from backgrounds and majors outside of computer science, data science, math, statistics, etc. We also especially encourage students from underrepresented minorities to participate.
Hands-on programming labs are a core part of our curriculum, so having some programming knowledge (specifically Python) will help participants get more out of the school. However, programming knowledge is not required; the school will include a track for participants who are completely new to programming. Experience with typical undergraduate math (calculus, linear algebra) and statistics (intro probability) is also helpful, but not required.
All students are welcome to apply but since future iterations of this school will be held in-person we will try to prioritize students from NYC-area universities. The school will be run on Eastern Time although students outside this timezone are welcome to join.


Why should I participate?
Our goals for school participants are the following:
1. Introduce participants to machine learning and artificial intelligence: what is it and why they should study it.
2. Give participants a sense of what research and a career in machine learning is like in terms of subject matter and day-to-day work.
3. Present participants with a path to further study machine learning and pursue a career in the field.
To achieve these goals, we have designed our curriculum to give participants a view into cutting edge research and its applications in modern society, as well as hands-on experience with these concepts.


How is the school structured?
The school will consist of a mix of lectures, labs, research talks, group discussions, and office hours. Most days will run from 10am - 4pm ET and will include a morning lecture, followed by a lab, and concluding with a research talk or panel discussion with leading researchers. See the full schedule and list of speakers for details.


Sounds great! How much will it cost to participate?
The school is entirely free!


Sweet! How do I sign up?
Interested students should fill out this application. Additionally, we recommend students take this diagnostic test in advance to get a sense of the programming and math background involved. If the test is challenging, that's ok! The school includes a track for less experienced students.


The school is organized by the members of the Machine Learning for Language Lab and is an updated version of the original workshop last year.

Speakers

Sara Hooker

Pablo Samuel Castro

Carlos Fernandez Granda

Lerrel Pinto

Andrew G. Wilson

Jennifer Wortman Vaughan

Akash Srivastava

Chris Albon

Siddharth Srivastava

Jaan Altosaar

Naila Murray

Rosanne Liu

Marianne Monteiro

Apply here

Try this diagnostic test to choose your track!
Deadline December 10, 2020

Organizers

Sam Bowman

New York University

Kianté Brantley

University of Maryland

Phu Mon Htut

New York University

Swapneel Mehta

New York University

Nikita Nangia

New York University

Alex Wang

New York University