Learn the systems, tools, and logic behind machine learning and AI that influences our daily lives.
Thank you for your interest in the NYU Tandon School of Engineering Center for K12 STEM Education's summer programs. We hope you and your loved ones are doing OK. This is an unprecedented and difficult time, and as we navigate COVID-19 directives and closings, we will continue to operate the Center with the safety and well-being of all of our constituents foremost in our actions. The Tandon School of Engineering and New York University community is monitoring the situation daily, making adjustments, and considering what may or not be possible with respect to our summer STEM education programs, including online options where feasible.
As you consider applying, we want you to know about our program deposit refund policy. Should you be admitted and matriculate, we want to make clear the Center for K12 STEM Education will adhere to the following guidelines with respect to refunding deposits:
- If your program is cancelled, travel restrictions continue, flights are not available, or other ongoing COVID-19 related issues prevent you from attending, you will receive a full refund of your program deposit. This is true also for international students who, in addition to these reasons, are unable to obtain an F-1 visa.
- We are assessing whether programs can be converted to an online offering. In the event your offering does move online, you have applied with the expectation of an on-campus program, and you do not wish to participate online, you will receive a full refund of your program deposit. You will also receive a refund if we need to cancel or consolidate specific sessions, and you do not wish to exercise the option to switch.
With respect to NYU Housing's policies in this regard, please visit this Resident Life webpage.
If you have any additional questions or concerns, please reach out via email at firstname.lastname@example.org or by phone at 646-997-3524.
Apply now for summer 2020!
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.
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.
Syllabus & Curriculum
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.
The 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.
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.
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
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
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
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.
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).
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.
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.
Final Application Deadline: April 13th, 2020
- 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 (You will need to complete a separate application for housing after you apply to the program. This will be sent to you in an email.)
- Meal plan is available at additional cost of $340 (10 meals/week for two weeks) and is required with housing
Program QuestionsDo I need to have past experience with coding?
You must have some experience with a coding language in addition to completing Algebra 2 or equivalent.
Is there a minimum GPA requirement?
The GPA requirement is a minimum 3.0 or equivalent.
When are applications due?
Preferred Deadline: February 28th, 2020
Final Deadline: March 14th, 2020
Who are the teachers? What are their qualifications?
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.
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