Machine Learning (ML) | NYU Tandon School of Engineering

Machine Learning (ML)

K12 STEM Education
On Campus
Tuition-Based
laptop computer with hand built robot next to it

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Description

NYU Tandon's summer program for Machine Learning introduces students to computer science, data analyses, mathematical techniques, and logic that drive the fields of machine learningand artificial intelligence. Lessons cover fundamental knowledge behind video and image recognition technologies; interactive voice controls for homes; autonomous vehicles; real-time monitoring and traffic control; cutting-edge diagnostic medical technologies, and other technologies that are a part of our daily lives.

PROGRAM LENGTH Two-week summer program
Monday–Friday, 9 am-4 pm
ELIGIBILITY Must be Age 14+ the day program begins (minimum 15 years old for housing). Must have completed Algebra 2 or equivalent and have some programming experience. This program is open to Domestic and International Students
EXPENSES Tuition Fee - $3050
More details

Your Tuition Fee includes the following:

  • Course Fees - $2800
  • University Programs Fees - $50
  • NYU Service Fee - $100
  • NYU Events Fee - $100 ($50 per week)

Add-On Fees - NOTE: RATES MAY CHANGE FOR 2025

  • Optional Housing Fee $616.00 ($308 per week)*
  • Mandatory Meal plan with housing $192 (10 meals plus 30 DD)
  • For International Students - Please note there are additional fees associated with your visa process. Please refer to the Office of Global Services for details

Unfortunately, scholarships and financial aid are not available for this program at this time.

30% is due within 1 week of acceptance to hold your spot and the balance is due 1 week before your session start date.

Tuition deposit is non-refundable.

*For Housing:Students must be 15 years old to stay in on campus housing and 16 years old to use the on campus gym.

Prerequisites for Machine Learning Program:

The program is open to high school students who have had some programming experience in any language. it is recommended that students have successfully completed Algebra 2 & Trigonometry as a foundational requirement. Additionally, it is preferred for students to possess prior experience in Precalculus, and an added advantage would be enrollment or plans to enroll in advanced courses such as AP Statistics or AP Calculus. These guidelines ensure that participants are equipped with the necessary mathematical knowledge and skills to fully engage with the rigorous scientific curriculum, fostering a more enriching and rewarding experience in the program.

Coursework

Students learn core principles in ML such as model development through cross-validation, linear regressions, and neural networks. Participants will develop an understanding of how logic and mathematics are applied both to "teach" a computer to perform specific tasks on its own and to improve continuously at doing so along the way. A strong emphasis is put on students learning the principles of engineering problem-solving, and how these techniques can be used to tackle societal challenges.

Important Dates

2025 Program Dates

  • January 1 – Application Opens
  • April 21 – Applications Due
  • Session One – Orientation June 13 (via Zoom 4:00 PM - 5:30 PM EST): Program June 16 – June 27
  • Session Two – Orientation July 1 (via Zoom 4:00 PM - 5:30 PM EST): Program July 7 – July 18
  • Session Three – Orientation July 25(via Zoom4:00 PM - 5:30 PM EST): Program July 28 – August 8

My most favourite experience of attending this program was to get a taste of what goes on inside NYU and basically exposing myself to coding and all the possibilities if I pursue engineering/coding later in my life

Related Research

The Machine Learning program is an ongoing collaboration among Tandon faculty developing cutting-edge ML applications including Profs. Ludovic Righetti, Elza Erkip, Siddharth Garg and Chinmay Hegde

Application

coming soon

ML Keywords: Data Banks, Computer Sciences, Computer Engineering, AI, Artificial Intelligence, Sensors, Physical Computing, Microcontrollers, Circuitry, Electronics, Coding, Computational Analytics, Simulations

 

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