14 Machine Learning Summer Programs for High School Students
If you’re a high school student interested in machine learning, summer programs can provide a structured way to explore how computers learn from data and make decisions. These programs typically introduce core concepts such as data analysis, model building, and basic algorithms while helping you develop practical coding and problem-solving skills. This kind of hands-on experience helps you move beyond theory and understand how machine learning is applied in real contexts.
What are the benefits of a machine learning program?
Machine learning summer programs focus specifically on how computers learn from data and make decisions. Through guided projects, you might work with datasets, build simple predictive models, or explore applications such as image recognition and natural language processing. Some programs emphasize research and independent projects, while others focus on structured coursework and guided instruction.
In this blog post, we’ve compiled a list of 14 machine learning summer programs for high school students.
If you’re looking for online summer programs, check out our blog here.
1. MITES Summer
Location: Massachusetts Institute of Technology, Cambridge, MA
Cost: Free (students pay only for transportation)
Dates: Late June – Early August (6 weeks)
Deadline: Typically early February
Eligibility: U.S. citizens or permanent residents in grade 11
MITES Summer is a highly selective, six-week residential program that immerses high school juniors in a rigorous STEM-focused academic experience. Students complete a demanding schedule of math, science, and humanities coursework, alongside electives that may include machine learning, genomics, and engineering applications. The program combines intensive academics with lab tours, college advising, and mentorship, helping students understand both the intellectual and practical pathways into STEM fields. Participants live on campus and experience the pace and expectations of college-level study while working closely with motivated peers. Although MITES is not exclusively focused on machine learning, its advanced STEM curriculum and exposure to emerging technologies make it a strong option for students interested in pursuing machine learning.
2. Veritas AI
Location: Online
Cost: Varies depending on program type; financial aid is available
Dates: Multiple cohorts offered year-round (including summer)
Deadline: Rolling. Spring (January), Summer (May), Fall (September), and Winter (November). You can apply to the program here.
Eligibility: High school students. AI Fellowship applicants should either have completed the AI Scholars program or exhibit experience with AI concepts or Python.
Veritas AI, founded and run by Harvard graduate students, offers programs for high school students who are passionate about artificial intelligence. Students looking to get started in AI, ML, and data science would benefit from the AI Scholars program. Through this 10-session boot camp, students are introduced to the fundamentals of AI & data science and have the opportunity to work on real-world projects. Another option for more advanced students is the AI Fellowship with Publication & Showcase. Through this program, students have the opportunity to work 1:1 with mentors from top universities on a unique, individual project. A bonus of this program is that students have access to the in-house publication team to help them secure publications in high school research journals. You can also check out some examples of past projects hereand read about a student’s experience in the program here.
3. NYU - ARISE
Location: Brooklyn, NY
Stipend: $2,000 stipend
Dates: June 1 – August 14
Deadline: February 27
Eligibility: Rising 11th-12th grade students who are full-time NYC residents attending NYC schools
NYU ARISE is a 10-week summer research program that provides high school students with hands-on experience working with faculty and researchers at the NYU Tandon School of Engineering. The program begins with several weeks of remote workshops focused on research methods, technical skills, and college preparation, followed by in-person lab placements in which students contribute to ongoing projects across STEM fields, including machine learning and data science. Students work in university research labs under faculty mentorship and present their findings at a final research colloquium and poster symposium. The program provides extensive lab experience, research exposure, and mentorship, making it a strong option for students interested in applying machine learning within real-world scientific research.
4. Lumiere Scholars Program - Machine Learning
Location: Online
Cost: Varies depending on program type; financial aid is available
Dates: Multiple cohorts year-round (including summer)
Deadline: Rolling deadlines (summer priority deadlines typically in March)
Eligibility: High school students
Lumiere Education’s Machine Learning track is part of its 1:1, research-based programs, in which high school students work individually with PhD mentors to explore advanced topics in machine learning and artificial intelligence. Over 12 weeks or more, students develop an independent research project, often focusing on deep learning, data science, or applied machine learning, and produce a structured research paper. The program includes regular mentor meetings, research workshops, and writing support, guiding students through the full research process from idea development to final presentation. This track is best suited for students who want a deeper academic experience in machine learning and the opportunity to conduct independent research with expert guidance!
5. Stanford AIMI Summer Research Internship
Location: Virtual
Cost: $2,400 + $45 application fee
Dates: June 15 – 26 | July 6 – 17
Deadline: February 21
Eligibility: U.S.-based high school students (grades 9-12), age 14+
The Stanford AIMI Summer Research Internship is a two-week virtual program focused on the applications of machine learning in healthcare and medicine. Students learn core concepts through lectures from Stanford researchers and clinicians, covering topics such as medical imaging AI, datasets, model evaluation, and responsible AI. Participants work in small teams on mentored research projects, applying machine learning ideas to real-world medical challenges while developing data analysis and problem-solving skills. The program combines technical learning with collaborative research, making it a good fit for students interested in the intersection of artificial intelligence, data science, and healthcare.
6. Columbia University - Data Science and Machine Learning II
Location: Online or New York, NY
Cost: $3,960 (online) | $12,449 (residential)
Dates: Session A (in-person): June 29 – July 17; Session B (in-person): July 21 – August 7 | Session C (online): July 6 – 17; Session D (online): July 20 – 31
Deadline: Varies by session
Eligibility: High school students in grades 9-12 with prior programming experience
This advanced course introduces students to machine learning and data science through a structured, hands-on curriculum that builds on prior Python knowledge. Students explore key mathematical foundations, including statistics, probability, and linear algebra, while learning to analyze datasets and create visualizations. The program emphasizes practical applications of machine learning across fields like technology and healthcare, while also encouraging students to think critically about fairness and responsible data use. Through guided projects and coding exercises, participants develop stronger programming skills and learn to approach real-world problems with a data-driven mindset. This course is best suited for students who already have some background in Python or introductory data science and want a more rigorous classroom-style experience.
7. Kode with Klossy Summer Camps
Location: Online
Cost: Free
Dates: June 1 – 12 | July 6 – 17 | July 20 – 31 | August 3 – 14
Deadline: March 31
Eligibility: Students aged 13-18 from underrepresented genders in STEM; no prior coding experience required
Kode With Klossy’s Machine Learning camp is a two-week intensive program that introduces students to the foundations of artificial intelligence and machine learning in an accessible, beginner-friendly environment. Participants learn core concepts such as algorithms, datasets, and natural language processing while building hands-on projects, such as training a simple chatbot in Python. Alongside coding instruction, students become part of a collaborative learning community that continues beyond the camp through its alumni network. This program is especially suitable for beginners who want an approachable introduction to machine learning without prior programming experience.
8. Stony Brook University Pre-College - Machine Learning and Self-Driving
Location: Stony Brook University, New York, NY
Cost: Residential $2,250 | Commuter $1,250
Dates: Summer (1 week; specific dates vary by session)
Deadline: May 15
Eligibility: U.S. residents aged 15-17; rising juniors or seniors in good academic standing
Stony Brook University’s Machine Learning and Self-Driving program introduces students to artificial intelligence through a focused week of lectures and experimentation. Participants explore the fundamentals of machine learning, neural networks, and data analysis while working directly with radio-controlled vehicles designed for the course. Students collect data, train neural networks using PyTorch, and test models that allow RC cars to drive autonomously, while also learning about architectures such as CNNs and ResNet. The program combines conceptual learning with applied experimentation, offering a concrete introduction to how machine learning systems function in real-world applications.
9. Machine Learning - NYU Tandon Summer Program
Location: NYU Tandon School of Engineering, Brooklyn, NY
Cost: $3,180 (tuition and required fees); housing and meals additional
Dates: Session 1: June 15 – 27 | Session 2: July 6 – 17 | Session 3: July 20 – 31
Deadline: April 17 (Session 1) | May 1 (Sessions 2–3)
Eligibility: Students aged 15+ in grades 9-12; open to U.S. and international students; background in precalculus and some programming required
The Machine Learning summer program at NYU Tandon School of Engineering introduces high school students to the mathematical and computational foundations of machine learning through an intensive two-week course. Students grasp fundamental concepts like model validation, neural network fundamentals, and data analysis through practical programming exercises and applied projects. The curriculum links machine learning ideas to real-world technologies such as image recognition, voice assistants, autonomous vehicles, and medical diagnostics. Via lectures and teamwork, participants develop both technical abilities and a clear understanding of how machine learning systems are created and assessed.
10. Beaver Works Summer Institute – Medlytics
Location: Online + in person at the Massachusetts Institute of Technology, Cambridge, MA (summer component)
Cost: $2,350 (free for qualifying families)
Dates: Online course begins February; 4-week summer program (dates vary)
Deadline: Not specified
Eligibility: High school students residing in the United States; grades 9-11
The Medlytics course at the Beaver Works Summer Institute explores the intersection of machine learning, data science, and medicine through a structured two-part experience. Students begin with an online prerequisite course that covers probability and statistics, Python programming, and introductory machine learning concepts, using real medical datasets. Those selected for the summer component participate in an intensive four-week program in which they apply machine learning techniques to physiological and time-series data under the guidance of clinicians and data scientists. The curriculum includes case studies, guest lectures, and collaborative challenges that build toward a final capstone project.
11. Northwestern Pre-college Online: Artificial Intelligence
Location: Online
Cost: $1,895
Dates: Multiple 2-week and 4-week sessions available
Deadline: Rolling admissions
Eligibility: Ages 13+
Artificial Intelligence: Navigating the AI-Powered Future is an introductory online course that helps students understand how AI systems work and how they shape everyday technologies. The program covers core concepts such as pattern recognition, neural networks, language models, and the basics of training and evaluating machine learning systems. Students explore practical applications through no-code AI tools, building simple prototypes while learning how to plan data use and technical requirements. The course also emphasizes responsible AI by examining ethical concerns such as bias, transparency, and social impact. A final capstone project allows students to design an AI-based idea or prototype and reflect on its real-world implications.
12. BeSMART (Berkeley Engineering Summer Machine-learning & AI Research Training) – UC Berkeley
Location: UC Berkeley, Berkeley, CA (residential)
Cost: $7,000; scholarships available
Acceptance Rate/Cohort Size: 20 students per cohort; competitive
Dates: July 27 – August 7
Application Deadline: April 15
Eligibility: High school students ages 15–17; no prerequisite coursework required
BeSMART is a two-week residential program at UC Berkeley's College of Engineering where students build hands-on fluency in Python, data analytics, and machine learning, taught by IEOR faculty. Each day is split between a morning lecture and an afternoon coding lab, where you work directly with real datasets and learn to extract and communicate insights through visualization. The program covers both foundational machine learning concepts and more advanced applications, with a focus on understanding how algorithms work rather than simply using them. It concludes with a final project in which you apply your skills to a domain of your choosing, alongside visits to Berkeley student labs and Silicon Valley companies throughout the two weeks.
13. ICS Summer Academy – UC Irvine
Location: UC Irvine, Irvine, CA (commuter; transportation provided from Anaheim and Santa Ana)
Cost: $2,500; need-based scholarships available; 10% discount for returning participants and second-course registration
Acceptance Rate/Cohort Size: Not specified
Dates: Session II: July 20 – July 31
Application Deadline: April 12
Eligibility: Rising high school students in grades 9–12 and graduating seniors; no prerequisites required
The AI and Machine Learning course at UCI's ICS Summer Academy is a two-week commuter program taught by faculty and graduate students from UC Irvine's Donald Bren School of Information and Computer Sciences. The course covers machine learning algorithms and methods, including classification, clustering, and regression, through both paper and programming approaches, combining morning lectures with afternoon group discussion. All materials, a laptop, and lunch are provided. The session concludes with a capstone project where students apply their skills to a real-world problem and present their findings to peers, parents, and faculty.
14. Computer Science Summer Institute (CSSI) – UCLA
Location: UCLA, Los Angeles, CA (commuter; no housing provided)
Cost: $3,150 (Certificate of Completion); $3,821 (Course Credit, domestic); scholarships available
Acceptance Rate/Cohort Size: Not specified; rolling admissions
Dates: Session A3 (Track 2): June 22 – July 10; Session B3 (Track 3): July 13 – July 31
Application Deadline: April 30 (Track 2); June 1 (Track 3)
Eligibility: High school students in grades 10–12, minimum 3.5 GPA, ages 15+; open to domestic and international students
CSSI offers two machine learning tracks at UCLA's Samueli School of Engineering, taught by UCLA CS faculty and PhD students. Track 2, Intro to AI, covers the data science lifecycle, from data selection and cleaning and feature engineering to model selection and prediction, through a blend of theoretical and practical instruction. Track 3, Generative AI, goes further, covering neural networks, large language models, and multimodal generation through coursework adapted from UCLA's machine learning and NLP courses. Each track combines morning lectures with afternoon group discussion and concludes with a capstone project. Students can enroll in one track per session or take one from each.