Learn the systems, tools, and logic behind machine learning and AI that influences our daily lives.
Due to COVID-19, NYU has decided to continue to keep campus closed for the summer and move programs to an online platform. Machine Learning will be running remotely as an interactive online course for Summer 2020. NYU Housing will not be offered at this time.
This is a great opportunity to take advantage of our STEM offerings from home and we encourage you to consider this special educational opportunity.
NYU’s Tandon Summer Program in Machine Learning is a two-week online 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.
This program is overseen a by faculty from the Electrical and Computer Engineering and Mechanical Engineering departments and their graduate students. It offers a unique opportunity to learn directly from some of today's most innovative researchers in the field. Students will learn the core principles in machine learning such as model development through cross validation, linear regressions and neural networks. They 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.
The program 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
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.
By the end of the program, students should have:
1. An understanding of the basic principles of AI and machine learning and how this can be used to tackle real world problems
2. Gained useful skills to formulate and solve machine learning problems
3. Hands-on experience in programming to solve machine learning problems (data analysis, use of machine learning algorithms, analysis of results, etc
How Will Online Learning Happen?
Instructors and students will expect to spend at least 5 hours a day in online instruction. The program will consist of daily face-to-face meetings between instructors and students complemented with time spent working on the assignments, watching additional instructional videos, working with online content specifically developed for the program and engaging with other students through online weekly projects. Instruction will consist of online interactive lectures followed by practical sessions (e.g. problem formulation exercises, programming, weekly projects). We anticipate to conduct lectures twice a day, in the morning and afternoon, each lecture followed by practical assignments supervised by the instructors. There will also be sessions where students will share the results of their work and collaborate with each other to solve more complicated problems.
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. For more information, check our Informational Page.
Program Details & Materials
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 Friday before beginning at 3pm.
Applications accepted on rolling basis, preferred May 25th deadline.
- Tuition: $2,000 per two-week session
Access to a computer and a good internet connection are the only requirements to participate in the program. All the material will be hosted online and easily accessible from a web browser and any additional software tool will be made freely available to the students.
If you have deposited before 4/17, and choose to withdraw, you will receive a refund of the tuition deposit. The deadline to withdraw will be May 15th. In order to withdraw you will have to log into the application portal where you can access the form to begin the process.
If you have deposited after 4/17, you will have up to 2 weeks before the start of the program to withdraw via the application portal to receive a full refund.
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?
Applications accepted on a rolling basis, but we would prefer it by May 25th.
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.