Application deadline
15 May 2026
General Pre-College deadline, extended from the earlier date.
Hybrid Program
Machine Learning for Physics and Astronomy: A New Way to Explore the Universe
Explore machine learning, physics, and astronomy through independent research at Brown, developing a scientific question and presenting your findings.
Last verified: 9 Oct 2026Reviewed by:SSSean Stevens
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Machine Learning at a glance: cost, dates and eligibility
Current status
Upcoming
Eligibility
Completing grades 10-12
Age range / Year group
Ages 16-18
Location / Region
Providence, Rhode Island, United States
Cost
$10,858; $8,902
Duration
5 weeks
Format
Hybrid
Artificial IntelligenceSpace & physics
Accommodation: Single, double, or triple residence rooms
Meals: Residential: breakfast, lunch, and dinner
Certificate / Outcome: Digital certificate; Course Performance Report
Latest verified cycle: 2026
Sessions / cadence: Online 1-3 live/week, 15-20h/week; campus 3h/weekday +2-3h/day homework
Mentor model: Course instructors and office hours
Main deliverable / output: Research project and symposium poster
Machine Learning summary
Brown's course connects machine learning with physics and astronomy through independent scientific research. A previous participant investigated black holes.
You move from literature review and research design to carrying out an investigation and presenting findings at a symposium.
It suits students who enjoy open-ended scientific questions and want to experience research that involves uncertainty, revision, and independent work.
What is Machine Learning for Physics and Astronomy: A New Way to Explore the Universe?
Machine Learning for Physics and Astronomy: A New Way to Explore the Universe is a Brown University Pre-College Course-Based Research Experience. It brings machine learning into the study of physics and astronomy through an independent research project.
The course combines online preparation with research on Brown's campus. Students review literature, develop a hypothesis and experimental design, then investigate their question and present findings at the CRE Symposium. A previous student's black-hole research illustrates the kind of scientific inquiry the course can support.
Why Succeed highlights Machine Learning
The research sequence carries students from scientific reading to designing and conducting their own investigation.
The combination of machine learning, physics, and astronomy offers a focused way to explore overlapping academic interests.
The CRE Symposium gives students a concrete reason to explain their findings clearly.
Brown explicitly treats setbacks and revision as part of authentic research.
Who is Machine Learning for?
Best for
Students drawn to both computational methods and questions about the universe, who want to develop an investigation rather than only attend lectures.
Not ideal if
You prefer predictable answers or cannot protect substantial time for independent work and required class participation.
Is Machine Learning worth it?
This course is worth considering if you want to test whether scientific research suits you. Developing a question, revising an investigation, and communicating findings can reveal more about your interests than subject exposure alone.
The substantial financial and time commitment deserves careful comparison with other options. The course is non-credit, and Brown says Pre-College is not a prospecting program for undergraduate admission. Its strongest value lies in the research experience and your ability to reflect on what you learned.
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A practical overview of cost, accommodation, meals, dates and provider details to review before deciding.
Cost and what is included
$10,858; $8,902; includes Residential housing for the three-week campus phase., Residential breakfast, lunch, and dinner daily., One campus meal daily for commuters., Fitness, athletic, and aquatics center access during campus study., Program activities and excursions..
Accommodation
Single, double, or triple residence rooms
Meals
Residential: breakfast, lunch, and dinner
Provider details
Review the Brown Pre-College Programs provider page and official sources before making a final decision.
Dates and logistics
5 weeks; Providence, Rhode Island, United States.
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 programsHow much preparation does Machine Learning need?
Effort level
High, with sustained independent research.
Best started
Allow at least 4-6 weeks before a future application deadline for essays, records, language testing, and family planning.
Main challenge
Turning a broad scientific interest into a manageable research question.
Typical preparation
Draft your application essay around a specific academic interest and your reasons for exploring it.
Gather both academic years' grades and arrange English-language testing if required.
Review a few introductory research articles and practice recording questions and citations.
Reserve time for course orientation, daily study, and campus travel before committing.
Useful if
You want to understand how scientific investigations develop and change.
What do students do on Machine Learning?
You develop an independent investigation through Brown's CRE research sequence. Preparation builds toward a campus project and a poster presentation of your findings.
Step-by-step process
- 1
Complete online orientation and learn the course's research expectations.
- 2
Study foundational content and research methodologies.
- 3
Review literature to identify a possible scientific question.
- 4
Work with instructors to develop a hypothesis and research design.
- 5
Conduct and refine your investigation during the campus phase.
- 6
Present your findings at the research poster symposium.
Machine Learning dates and deadlines
Next cycle not announced yet. These dates are from the latest verified cycle and should be used as a reference only.
Reference date
Programme dates
22 June-24 July 2026
Applications open
14 January 2026
General Brown Pre-College application cycle.
Final admission decisions
22 May 2026
General Pre-College admissions milestone.
Campus check-in
5 July 2026
9 a.m.-1 p.m. ET at Sayles Hall.
| Milestone | Date | Timezone | Status | |
|---|---|---|---|---|
Application deadline | 15 May 2026 | Local | Reference date | |
Programme dates | 22 June-24 July 2026 | Local | Reference date | |
Applications open | 14 January 2026 | Local | Reference date | |
Final admission decisions | 22 May 2026 | Local | Reference date | |
Campus check-in | 5 July 2026 | Local | Reference date |
Eligibility and requirements
Completing grades 10-12.
Ages 16-18 by 15 June 2026.
Strong English-language proficiency.
Admission to Brown Pre-College before separate course enrollment.
Meet any prerequisites in the individual course description.
How much does Machine Learning cost?
Residential program fee
$10,858 — Includes campus housing and meals.
Funding or discounts
- Commuter program fee: $8,902 — Includes one campus meal daily.
- Application fee: $80 — Non-refundable; eligible applicants may request a waiver.
- Enrollment deposit: $500 — Normally non-refundable; required before course enrollment.
- Scholarships: Eligibility restrictions apply — Separate applications are required for Sibley or PPSD consideration.
What's included
Residential housing for the three-week campus phase., Residential breakfast, lunch, and dinner daily., One campus meal daily for commuters., Fitness, athletic, and aquatics center access during campus study., Program activities and excursions.
How to apply to Machine Learning
- 1
Create one applicant account in Brown's Student Portal.
- 2
Complete the application form and 250-500-word essay independently.
- 3
Upload current and previous academic-year grades and pay the application fee or request a waiver.
- 4
Submit any required language results, recommendations, or additional materials.
- 5
Monitor your portal and email for application requirements and the admission decision.
- 6
If admitted, confirm the course before paying the enrollment deposit.
- 7
Enroll separately in the course through your Student Portal catalog.
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What does Machine Learning cover?
The program covers the following focus areas.
Machine learning, physics, and astronomy
Explore the intersection of these subjects through an independent scientific investigation.
Literature review
Read existing research to identify questions worth investigating.
Hypothesis and research design
Develop a hypothesis and plan an investigation with instructor guidance.
Research communication
Present and explain your findings at the CRE research symposium.
Curriculum may be adjusted based on participant interests and current developments.
What is a typical day on Machine Learning?
A typical day during the program.
Online study
Complete asynchronous work and plan for 15-20 hours of course activity each week.
Campus classes
Attend three hours of class each weekday.
Campus independent work
Complete at least 2-3 hours of homework, research, or project work each day.
Who teaches on Machine Learning?
Course instructors help students develop a novel research project and guide the research process. Instructor office hours offer opportunities to discuss content, assignments, and questions.
Where do students stay on Machine Learning?
Residential plan
- Residence hall rooms: Single, double, or triple rooms include furniture, wireless internet, and air conditioning.
- Three daily meals: Breakfast, lunch, and dinner are served in all-you-care-to-eat dining halls.
- Residential support: Resident Assistants support students, with nightly face-to-face check-in at 10 p.m. ET.
- Campus recreation: Students can use fitness and aquatics facilities and participate in program activities.
Non-residential plan
- Commuter option: Attend campus classes and activities while living elsewhere.
- Commuter meal and facilities: The commuter fee includes one campus meal daily and fitness, athletic, and aquatics access.
Roommate requests are not honored. Students arrange their own travel and bring their own linens.
What happens after Machine Learning?
After completing this program, participants often pursue:
Complete an independent scientific investigation.
Present research findings at the CRE Symposium.
Receive a digital Certificate of Completion after successful completion.
Receive an instructor Course Performance Report.
The course is non-credit, and Brown Pre-College is not a prospecting program for undergraduate admission.
Where does Machine Learning take place?
The campus phase takes place at Brown University in Providence, Rhode Island. Families coordinate transportation to and from campus, with check-in at Sayles Hall on the Main Green.
Campus housing is provided only for the in-person phase of the residential option.
Learning format
The online phase uses Canvas and combines asynchronous content with one to three scheduled live sessions each week, requiring daily login and 15-20 hours of weekly work. The campus phase includes three hours of class each weekday and at least two to three hours of homework daily.
Online orientation takes a few hours at your own pace during the preceding week.
Mentorship and feedback
Work directly with instructors to develop a novel research project.
Use instructor office hours to discuss questions and assignments.
Request Writing Center support for writing assignments.
Access English-language support by appointment.
Receive an instructor Course Performance Report after completion.
Time-zone considerations
- The online phase includes 1-3 pre-scheduled live sessions weekly.
- Daily login and asynchronous content are required.
- Reserve 15-20 hours weekly for online course activities.
- Campus check-in runs from 9 a.m. to 1 p.m. ET.
Tech requirements
Computer with a reliable internet connection.
Webcam and microphone, built-in or external.
Word processor such as Microsoft Word or Google Docs.
PDF reader such as Adobe Acrobat.
Zoom for the blended online component.
Assessment style
Assessment centers on research participation, project work, a symposium presentation, and an instructor Course Performance Report.
- Complete readings, assignments, projects, and assessments on time.
- Participate actively with instructors and classmates.
- Present a research poster at the symposium.
- Receive narrative instructor feedback through the Course Performance Report.
- Earn the completion certificate through successful course participation.
Portfolio / outputs
Main deliverable / output
The main output is an independent research investigation with findings shared at the CRE Symposium.
Research poster presented at the CRE Symposium.
Digital Certificate of Completion after successful completion.
Instructor Course Performance Report.
Machine Learning FAQs
The course connects machine learning with physics and astronomy through independent research. A previous participant researched black holes.
Yes. CRE students conduct a literature review, develop a research question and hypothesis, design their investigation, and complete an independent project.
The first two weeks take place online, followed by three weeks at Brown University. The online component combines asynchronous work with one to three scheduled live sessions each week.
Plan for 15 to 20 hours weekly during the online phase. On campus, classes last three hours each weekday, with at least two to three hours of homework each day.
Brown Pre-College courses are non-credit. Successful completion earns a digital Certificate of Completion, and CRE students also receive a Course Performance Report.
No. Admission and course enrollment are separate. Students should confirm their preferred course before submitting the non-refundable $500 enrollment deposit.
Brown expects students to complete the application largely independently. Parents and guardians can support planning, but submitted application materials must be the student's original work.
During the campus phase, residential fees include housing, three meals daily, and fitness, athletic, and aquatics access. Rooms may have single, double, or triple occupancy.
Alternatives to Machine Learning
Compare these options if your main interest is artificial intelligence, space science, or a broader physics course.
Alternatives to Machine Learning
Online study focused on artificial intelligence
Artificial Intelligence
Deadline: Not announced
From $3200
15-17
Online
Online
Best for
Students prioritizing AI over astronomy
Online study of orbital mechanics and aerodynamics
Space & physics
Deadline: Not announced
From $3200
14-17
Online
Online
Best for
Students drawn to space engineering
In-person summer study focused on physics
Space & physics
Deadline: Not announced
From £5495
13-17
Cambridge
In-person
Best for
Students prioritizing physics and mathematics
Save this program in Succeed to compare it with similar programs, dates, costs and formats in one place.
Compare with other programsContent 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.
Sources & verification
Source types reviewed
- Handbook of Pre-College Policies | Pre-College Program | Brown University
- Summer@Brown Hybrid Course-Based Research Experience (CRE) | Pre-College Program | Brown University
- Program Dates and Costs | Pre-College Program | Brown University
- Course-Based Research Experiences (CRE)
- Frequently Asked Questions (FAQ) | Pre-College Program | Brown University
- Financial Policies and Payment Information | Pre-College Program | Brown University
- Application Fee Waivers | Pre-College Program | Brown University
- Admissions Decisions | Pre-College Program | Brown University
- Application Checklist | Pre-College Program | Brown University
- Apply | Pre-College Program | Brown University
- 2026 Program Costs: Breakdown of Fees
- Pre-College Program | Brown University
Succeed uses official provider information where available, but keeps this public page focused on comparison and planning inside Succeed.
What we verified (on 9 Oct 2026)
Course identity checked
Eligibility reviewed
Historical dates checked
Residential and commuter fees checked
Workload and support reviewed
Completion credentials checked
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.
Machine Learning for Physics and Astronomy: A New Way to Explore the Universe
by Brown Pre-College Programs
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