Machine Learning | NYU Tandon School of Engineering

Machine Learning

Three Sessions: June 22 - July 3rd, July 13 - July 24, and August 3rd - August 14th!

Software analyzing vehicles driving by

Summer 2019 applications are now closed, but sign up for our mailing list for updates!

NYU’s Tandon Summer Program in Machine Learning is a two-week, full-day summer program to introduce high school students to the computer science, data analyses, mathematical techniques and logic that drive the fields of machine learning (ML) and artificial intelligence (AI).  People are experiencing new and always improving applications of these fields every day: in video and image recognition technologies; interactive voice controls for homes; autonomous vehicles; real-time monitoring and traffic control; cutting-edge diagnostic medical technologies; and in ever more aspects of our daily lives.

student and teacher coding Started by Tandon faculty in the Electrical and Computer Engineering and Mechanical Engineering departments, this program will by led by these experts and their graduate students offering a unique opportunity to learn directly from some of today's most innovative researchers.  Students in the program can anticipate completing it with new knowledge concerning core principles in machine learning such as model development through cross validation, linear regressions and neural networks, as well as 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.

The Machine Learning program will take place at the NYU Tandon School of Engineering campus in Downtown Brooklyn.  It is suited for academically strong students who have an interest in computer and data science and the ways in which they are used in society to develop new capabilities, services and products.  No prior experience in computer science is required.

Curriculum Overview

After creating many successful STEM education programs for students and teachers, the NYU Tandon Center for K12 STEM Education has used its experience to develop a two-week program to explore the fields of machine learning and artificial intelligence. Through the active engagement with and manipulation of real-world data sets -- like an image, video or audio archive, medical images or even closed captions from TV shows -- students will learn the basics of data analysis, visualization, and how to build and evaluate machine learning models.  

classroom of students using laptopsThe program’s curricular design keeps in mind that learning by doing is the best way to teach new concepts. Throughout the ten days of instruction the focus will be on providing sequenced, interactive experiences and activities that forge a base of knowledge and technical skills so students can build confidence and take on increasingly complex challenges and tasks.  Students will create simple deep networks, one of the current and powerful methods driving artificial intelligence research and applications, gain exposure to coding languages like Python and Toolkit for Machine Learning, complete programming exercises and finish the workshop with their own open-ended project.

Learning Outcomes

Students will learn the art and science of Machine Learning from the foundational mathematics to state-of-the-art models. This theory is brought to life by daily assignments and weekly projects that require programmatic implementation of machine learning algorithms. 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. Students are exposed to higher levels of mathematics, computer and data-science, and electrical engineering in relation to machine learning. They complete the course with the confidence to explore these topics further and apply them to other areas of interest themselves. 

Who Can Apply?

  • High school students who have successfully completed Algebra 2 or equivalent and have had some programming experience in any language
  • Academically prepared, highly motivated students who are willing to take initiative and have achieved a minimum 3.0 GPA or equivalent
  • Applicants with a passion for science, technology, engineering, and math

*International students are welcome to apply but should be aware they are required to submit proof of English language proficiency and apply for a student visa.  For more information check out the FAQ below. 

Program Details

Program Sessions

Choose one of the following sessions when you apply *

  • Session 1: June 22, 2020 - July 3rd, 2020 
  • Session 2: July 13, 2020 - July 24, 2020  
  • Session 3: August 3rd, 2020 - August 14, 2020

*Orientation for all sessions will be the Sunday before beginning at 4pm.

Application Time Frame TBD

Program Costs

  • Tuition: $2,000 + $100 Program Fee (special events and activities) per two-week session
  • Housing is available at an additional cost of $558 for 2 weeks
  • Meal plan is available at additional cost of $340 (10 meals/week for two weeks) and is required with housing

Questions? Check out our FAQ below or contact us at or 646.997.3524 

students giving a presentation


Day 1: Introduction to Machine Learning

  • Introduction to basics of linear algebra and probability

  • Programming in Python

  • Introduction to Machine Learning 

  • Overview of Regression and Classification problems

Day 2: Linear Regression

  • Introduction to statistics

  • Basic machine learning models - Linear Regression

Day 3: Generalization Error

  • Polynomial Regression

  • How to generalize the machine learning models 

  • Methods to improve the performance of ML models

Day 4: Classification

  • Limitations of linear regression for classification

  • Introduction to optimization - Gradient Descent

  • Logistic Regression

  • Multi - class classification

Day 5: Mini-Projects \ Competition and Presentations

  • Compete with other groups of classmates on producing the best performing model on a given dataset

  • -or- Implement a regression/classification model on a non-ML dataset of your own finding

  • Present work to the class

Day 6: Neural Networks

  • Why are neural networks so powerful?

  • Understanding different hyperparameters

  • Getting comfortable with Tensorflow and Keras 

Day 7: Convolutional Neural Networks

  • Significance of convolutional neural networks

  • How computers think of images?

  • Image classification using CNN’s

Day 8: Deep Learning and Applications of CNNs

  • Regression and Classification

  • Denoising

  • Segmentation

  • Transfer Learning

  • Generative Adversarial Networks

Day 9: Final Projects

  • What issues in your community, country, world interest you? How can you use the ML methods so far to gain insight into the data surrounding these problems?

  • Share ideas with the class

  • Gather data and formulate a ML model that will help you answer the question above.

  • Begin implementation.

Day 10: Final Projects (continued) and Presentations

  • Students present the background to their chosen problem and the details to their implemented machine learning model.

Examples of Final Projects:

  • Doodle Recognition (with user image input)

  • Real time human emotion classification with webcam input

  • Image denoising with U-Net architecture

  • Semantic Segmentation for self-driving cars

  • Image inpainting with model comparison (U-Net and Sequential Autoencoder).


Financial Questions

The total cost for tuition $2,000.00 plus $100.00 to cover program fees. We ask for $1,000.00 to be paid at the time of acceptance in order to officially hold your spot. The other $1,100.00 will be due by the week before your session start date. The tuition deposit is non-refundable.

Unfortunately, we do not offer scholarships or financial aid at this time. However, the K12 STEM Center does offer many free programs which you may qualify for. 

Program Questions

You must have some experience with a coding language in addition to completing Algebra 2 or equivalent. 

The GPA requirement is a minimum 3.0 or equivalent. 

There is not official due date for applications. However students are accepted on a rolling basis so the sooner you apply the more chance you have of obtaining a spot!

Our programs are overseen by Tandon faculty, and we recruit current engineering and computer science students to serve alongside these experts as teachers and mentors.  Every classroom will have a minimum of one graduate student instructor, and at least one additional instructor will be assigned to each class of (maximum) 24 students.   

Housing Questions

Yes! Students will have the option to live on campus in Othmer Hall with an additional cost. 

Housing is available at an additional cost of $558 for 2 weeks

Yes, female students will only share a room with other females and male students with other males. However the floors are coed, and each room has an enclosed bathroom. So female students may be living next door to male students. Additionally, roommate requests are not allowed at this time.

The meal plans available are:
17 meals + $30 dining dollars = $242/week
12 meals + $30 dining dollars = $187/week
10 meals and $30 Dining Dollars per week: $170/week (automatic for residential students)
8 meals and $30 Dining Dollars per week: $149/week (commuter students only)

Please note that residential students must have a minimum of 10 meals/week for their meal plan.

If you are living on campus, the required meal plan is 10 meals/week which will cover food for Monday - Friday. The meal plan also comes with $30 dining dollars per week which can be used the on campus cafe/shop. However students are encouraged to explore Brooklyn area and are welcome to eat in local restaurants on weekends. 

We will offer a variety of events and activities outside of class in the evenings and on weekends. These events are optional but highly recommended. Students are also given the option to explore the city during their free time, as long as they return before curfew. Parents should note that we are not a closed campus, therefore we cannot prevent anyone from leaving campus during the daytime. We encourage students and parents to discuss off campus exploration prior to the program to determine what you feel comfortable with in regards to leaving campus.

11pm Sunday through Thursday and 12am Friday and Saturday. 

International Students

We require two extra things: 

1. A student visa such as the F-1

2. Proof of English Language Proficiency: acceptable proofs include...

  • TOEFL iBT (Test of English as a Foreign Language Internet-Based Test)
  • IELTS Academic (International English Language Testing System) 
  • PTE Academic (Pearsons Test of English Academic) 
  • C1 Advanced or C2 Proficiency (Cambridge English: Advanced or Proficiency) 
  • MELAB (Michigan English Language Assessment Battery) 
  • iTEP (International Test of English Proficiency) 

Unfortunately no. TSP in ML is an academic program therefore we require a student visa such as an F-1 or J-1. B-1/B-2 or ESTA visas are not permitted. NYU’s Office of Global Services will be able to assist incoming students with obtaining their visas.

No, you qualify as long as you are currently residing in the United States and are attending an native-English speaking school.

Exemption will be given in the following circumstances: 

  • If your native language is English; If you have been studying in a school or college/university where the sole language of instruction is English for at least 3 full years at the time of your application; 
  • or If your education has been completed entirely in schools/colleges/universities where the language of instruction is English.  

For additional information including tips and tricks on navigating NYC and more details about on-campus facilities and services, check out our helpful blog below:

Summer 2019 Application (Closed)

The Summer 2019 Application is now closed, thank you all those who applied!