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    In-person Program

    Generative AI & Machine Learning High School Course

    by Summer Springboard

    A hands-on pre-college course in Python, data analysis, machine learning, neural networks and responsible AI, with practical projects and campus experiences.

    Last verified: 26 Aug 2026Reviewed by:SSSean Stevens
    Generative AI & Machine Learning High School Course

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    Generative AI Course at a glance: cost, dates and eligibility

    Current status

    Closed

    Eligibility

    Grades 9-12; 3.0 GPA

    Age range / Year group

    Ages 14-18

    Location / Region

    Berkeley; New York City

    Cost

    $3,298; $5,998

    Duration

    2 weeks

    Format

    In-person

    Latest deadline

    17 May 2026 · Next cycle not announced yet. These dates are from the latest verified cycle and should be used as a reference only.

    Get next-cycle reminder
    Artificial IntelligenceCoding & AIComputer ScienceStatistics

    Accommodation: Residential and commuter options

    Meals: Residential all meals; commuter lunch

    Latest verified cycle: 2026

    Main output: Applied final project

    Application time: About 30 minutes

    Generative AI Course summary

    This is an intensive introduction to Python, data analysis, machine learning, neural networks and the ethical questions raised by AI.

    Students work with real-world datasets, build models and complete a final applied project rather than studying the subject only through lectures.

    It best suits students who want to test computer science or data science through practical work while experiencing a structured pre-college campus environment.

    What is Generative AI & Machine Learning High School Course?

    The Generative AI & Machine Learning High School Course is a Summer Springboard pre-college program focused on practical artificial intelligence and data science. Students progress from Python fundamentals and exploratory data analysis to machine learning, neural networks and deep learning.

    Teaching centers on exercises and projects using real-world datasets, with ethical discussions about bias, fairness and societal impact. Students can combine the academic course with residential campus life or attend as commuters at Berkeley or Barnard College in New York City.

    Why Succeed highlights Generative AI Course

    • The curriculum moves from foundational Python to a working machine learning model and simple neural network.

    • Real-world datasets make the technical learning more concrete than a purely conceptual AI overview.

    • Dedicated attention to bias, fairness and societal impact adds responsible-AI context.

    • The final project gives students a tangible way to apply their learning.

    • Residential and commuter routes make the same course accessible through two different campus experiences.

    Who is Generative AI Course for?

    Best for

    High-school students who want hands-on exposure to Python, data science and machine learning before choosing a university subject or technology pathway.

    Not ideal if

    You want an accredited qualification, a low-cost introductory experience or a course centered only on generative-AI prompting.

    Is Generative AI Course worth it?

    It can be worthwhile for a student who learns best by building things and wants to test whether data science or AI feels like a serious academic interest. The progression from Python basics to models, neural networks and a final project gives the course a coherent technical arc.

    The main limitation is value for money: tuition is substantial, and the campus names should not be mistaken for university sponsorship. Families should weigh the practical curriculum and broader campus experience against lower-cost coding courses or independent projects.

    A practical overview of cost, accommodation, meals, dates and provider details to review before deciding.

    Cost and what is included

    $3,298; $5,998; includes Residential tuition includes the academic course, lodging and all meals., Residential tuition includes excursions and weekend excursions., Commuter tuition includes the academic course and lunch., Commuter programming runs 9:00am-5:00pm, Monday-Friday., Commuter tuition includes excursions and daytime programming., College-readiness workshops are included., Residential students receive evening activities..

    Accommodation

    Residential and commuter options

    Meals

    Residential all meals; commuter lunch

    Provider details

    Review the Summer Springboard provider page and official sources before making a final decision.

    Dates and logistics

    2 weeks; Berkeley; New York City.

    What to check before committing

    Confirm current dates, payment terms, travel arrangements, cancellation rules and what is included with the provider.

    Compare this program with similar options before deciding on dates, cost, format and fit.

    Compare with similar programs

    How much preparation does Generative AI Course need?

    Effort level

    Moderate to high academic intensity.

    Best started

    Begin application planning four to six weeks before the published admission deadline.

    Main challenge

    Absorbing new programming and statistical concepts quickly.

    Typical preparation

    • Refresh basic algebra and graphs before the course.

    • Try a short beginner Python tutorial to learn variables, loops and functions.

    • Prepare examples of why AI or data science interests you for the short-answer application.

    • Compare commuter and residential logistics before selecting a campus and tuition type.

    Useful if

    You have curiosity about coding, data or mathematical problem-solving.

    What do students do on Generative AI Course?

    Students learn through tutorials, coding exercises, dataset analysis and a continuing final project. The course builds technical complexity across its two weeks.

    Step-by-step process

    1. 1

      Learn Python control flow, loops, functions and basic data structures.

    2. 2

      Use pandas, NumPy, matplotlib and seaborn to prepare and visualize data.

    3. 3

      Conduct exploratory analysis to identify patterns in real-world datasets.

    4. 4

      Build and evaluate a basic machine learning model.

    5. 5

      Create and train a simple neural network with Keras or TensorFlow.

    6. 6

      Discuss bias, fairness and the wider social impact of AI.

    7. 7

      Apply the course material through a final project.

    Generative AI Course dates and deadlines

    Next cycle not announced yet. These dates are from the latest verified cycle and should be used as a reference only.

    General admission

    17 May 2026

    Latest verified application cycle

    Reference date

    Berkeley Session 2

    21 June-3 July 2026

    Two-week course session

    Reference date

    Berkeley Session 4

    5-17 July 2026

    Two-week course session

    Reference date

    Berkeley Session 6

    19-31 July 2026

    Two-week course session

    Reference date

    Barnard Session 1

    5-17 July 2026

    New York City course session

    Reference date

    Barnard Session 2

    19-31 July 2026

    New York City course session

    Reference date

    Eligibility and requirements

    • Open to students in Grades 9-12.

    • A 3.0 GPA or local equivalent is expected.

    • Students entering high school in fall may apply.

    • Recent spring high-school graduates may also apply.

    • International applicants must demonstrate English proficiency.

    • Applicants must satisfy the Essential Eligibility Criteria.

    How much does Generative AI Course cost?

    Program tuition

    $3,298; $5,998 — Commuter; residential

    Funding or discounts

    • Application fee: From $99 — Mandatory and nonrefundable
    • Program deposit: $600 — Paid during application
    • Need-based scholarship: Up to 30% residential — Up to 60% of commuter tuition
    • Eligible discounts: 5% — Heroes, multi-program, sibling or referral

    What's included

    Residential tuition includes the academic course, lodging and all meals., Residential tuition includes excursions and weekend excursions., Commuter tuition includes the academic course and lunch., Commuter programming runs 9:00am-5:00pm, Monday-Friday., Commuter tuition includes excursions and daytime programming., College-readiness workshops are included., Residential students receive evening activities.

    How to apply to Generative AI Course

    1. 1

      Open the online application portal and enter basic contact information.

    2. 2

      Verify the account through the email link and complete the account details.

    3. 3

      Select the campus, session, course and commuter or residential tuition type.

    4. 4

      Complete the student details and short-answer application with the student present.

    5. 5

      Sign the required agreement forms.

    6. 6

      Pay the $600 deposit and application fee.

    7. 7

      Submit the complete application for review.

    8. 8

      Monitor email for an enrollment decision and any additional forms.

    What does Generative AI Course cover?

    The program covers the following focus areas.

    Python foundations

    Learn control flow, loops, functions, data structures and programming fundamentals.

    Data analysis

    Prepare and explore real-world datasets using pandas and NumPy.

    Machine learning

    Build and evaluate practical models after analyzing and preparing data.

    Neural networks

    Build and train a simple neural network with Keras or TensorFlow.

    Responsible AI

    Examine bias, fairness and the societal consequences of AI systems.

    Curriculum may be adjusted based on participant interests and current developments.

    What is a typical day on Generative AI Course?

    A typical day during the program.

    9:00am

    Students attend their selected college-style academic course.

    1:30pm

    Students join a recreational activity or course-related academic excursion.

    3:00pm

    College-readiness or personal-development workshops follow.

    7:00pm

    Residential students take part in clubs or other evening activities.

    Who teaches on Generative AI Course?

    The Berkeley course identifies Dr. Kamal Ali, an AI and machine learning specialist with senior research and industry experience. The Barnard course identifies Christelle Scharff, a computer science professor whose work includes generative AI, machine learning and global software engineering.

    Instructor information is campus-specific and may change between cycles.

    Where do students stay on Generative AI Course?

    Residential plan

    • Campus housing: Students stay in supervised university dormitories or campus suites.
    • Meals: All meals are included and are usually served in campus dining halls.
    • Pastoral support: Program staff live in the residences and provide 24-hour emergency support.
    • Security: Residences use controlled access and receive 24-hour security patrols.

    Non-residential plan

    • Day attendance: Commuter students attend weekday programming from 9:00am to 5:00pm.
    • Commuter meals: Lunch is included during weekday programming.

    Berkeley rooms do not have air conditioning; Barnard suites may contain shared or single rooms.

    What happens after Generative AI Course?

    After completing this program, participants often pursue:

    Use the final project as evidence of sustained interest in AI or data science.

    Progress to more advanced Python, statistics or computer science study.

    Build on the course by developing independent machine learning projects.

    Explore university pathways in computer science, data science or artificial intelligence.

    Use career-exploration sessions to compare technical and nontechnical technology roles.

    The course does not state that it awards university credit or an accredited qualification.

    Where does Generative AI Course take place?

    Students can take the course at Summer Springboard’s Berkeley campus program in the San Francisco Bay Area or at Barnard College in New York City. Both options combine campus-based teaching with excursions and a broader pre-college experience.

    The provider states that these programs are not run by Berkeley or Barnard College.

    Generative AI Course FAQs

    Students progress from Python fundamentals and exploratory data analysis to machine learning, neural networks and deep learning. Ethical issues such as bias, fairness and societal impact are also covered.

    No. The documented course options are in-person campus programs in Berkeley and New York City.

    Yes. Students analyze real-world datasets, create visualizations, build machine learning models and work on a final project.

    The curriculum names Python, pandas, NumPy, matplotlib, seaborn, Keras and TensorFlow.

    Yes. Residential tuition includes lodging and all meals, while commuter students attend weekday programming and receive lunch.

    Applicants should be in Grades 9-12 and have a 3.0 GPA or local equivalent. Students must also meet the provider’s Essential Eligibility Criteria.

    The provider estimates approximately 30 minutes and says the student must be present while it is completed.

    No. Summer Springboard states that its programs at Berkeley and Barnard College are not run by those institutions.

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    Sean Stevens

    Content reviewed by

    Sean Stevens

    Co-founder, Succeed | Founder, Immerse Education (2012–2026)

    Sean works at the intersection of academic enrichment, program quality and university preparation, with expertise in evaluating pre-university experiences for ambitious secondary school students.

    View author page

    Sources and verification

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    • Generative AI & Machine Learning | Barnard College Course | Summer Springboard - Summer Springboard
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    Succeed uses official provider information where available, but keeps this public page focused on comparison and planning inside Succeed.

    What we verified (on 26 Aug 2026)

    • Course name verified

    • Curriculum reviewed

    • Locations cross-checked

    • Session dates checked

    • Tuition options checked

    • Eligibility reviewed

    • Application process checked

    • Policies reviewed

    How Succeed uses this information

    We use source material to verify core facts, then show older cycle dates as reference when a current cycle is not available. Always check current application instructions before applying.