Graduate Certificate In AI and Machine Learning

with an Astrophysics-AI Badge

One of our core missions is to increase training opportunities for the Astronomy community in AI/ML methods. We have partnered with the UT Austin Computer and Data Science Online program to create a new, accessible graduate training program that offers courses in AI/ML methods, including an AI-Astronomy application course.

Application information


The program equips professionals from all scientific fields with essential AI skills for data analysis, predictive modeling, and research.


The program fosters career advancement and long-term growth in AI-driven fields.

Partner

Program Format & Structure

  • It is a 100% online graduate certificate, accessible from anywhere.

  • Structured and flexible—on-demand lectures released weekly, and corresponding to a typical semester schedule.

  • The program is accredited through UT Austin, such that courses are transcripted and course credit is transferable to other programs.

  • The 12-credit credential can stand alone or stack onto additional online courses to complete a full 10 course MS degree in AI.

Course Planning

  • Requires 4 (3-credit) courses, including a Machine Learning class, a Deep Learning class, and two electives.

  • Topics will include application of machine learning and deep learning to astronomy data; curation of training sets; explainable AI methods; simulation-based inference techniques.

  • Students may take Machine Learning and Deep Learning concurrently, and they can plan their other coursework flexibly. Students must complete Machine Learning before enrolling in AI for Astrophysics. They may take Deep Learning concurrently with or prior to AI for Astrophysics. 

    Students with sufficient prior experience may take Machine Learning and Deep Learning during the same semester. Special approval is not required to take these courses together.

  • Yes. Taking one course per semester is common and is an appropriate way to complete the program, particularly for students balancing CAIML with employment, research, or other responsibilities.

    Students taking one course per semester remain in good standing with CAIML, provided they meet the program's other academic requirements.

  • CDSO does not differentiate between full time and parttime student status. Students are required to take one or more courses during each long semester (Fall and Spring) to participate in the program. Students are encouraged to register for the number of courses that best fit their personal and professional schedule.

    Students who need full-time status for a purpose outside CAIML—such as financial aid, immigration, employment benefits, or another institutional requirement—should confirm whether separate eligibility rules apply.

  • No. CAIML students are not required to take courses during the summer, although they may. Students are expected to enroll in at least one course during the Fall and Spring semesters unless they have arranged a Leave of Absence.

  • Email the CAIML Graduate Program Coordinator to request a Leave of Absence.

  • CAIML is designed to be flexible. Students may take as many or as few courses per semester as their schedules allow.

    CAIML students should plan to spend about 12–18 hours per week on each course. Students working full time or managing other significant commitments may therefore want to take one course per semester.

AstroAI Badge

  • Students may earn the Astro-AI badge by enrolling in the AI for Astrophysics course as one of the electives.

  • To earn the AstroAI Badge, students must earn at least a B in Machine Learning, Deep Learning, and AI in Astrophysics.

  • We currently anticipate offering it once per year. AI in Astrophysics is a new course, and its future schedule may depend on factors such as student demand and instructor availability.

    Students who need AI in Astrophysics to complete the AstroAI Badge should consider course availability when planning their remaining semesters.

  • Students can adjust the sequence of their other CAIML courses and take AI in Astrophysics when it is next available. Because CAIML allows flexibility in course sequencing, students do not necessarily need to delay all other coursework while waiting for the course to be offered.

Admissions & Eligibility

  • Applications are open to both national and international prospective students. There is no U.S./Texas residency requirement and no out-of-state tuition.

  • Undergraduate degree in a computer science related field, math, or natural science, which includes preparation in programming, statistics, and discrete  math. See Spring Application Guide for more information for more information.

    The Deep Learning and Machine Learning courses are required prior to enrolling in the AI for Astrophysics course.

  • Fall applications open December 15th - and the final deadline is April 15th. Spring applications open June 1st and the final deadline is September 1st.

  • There are typically additional registration opportunities before the semester begins, including a second registration period and late registration.

    Registration dates vary by semester, so students should consult the current CAIML and UT Austin registration schedule rather than relying on dates from a previous term.

  • Resolve the hold with the appropriate UT Austin office as soon as possible and plan to register during a subsequent registration period if necessary.

    For issues involving official undergraduate transcripts, students can contact the Office of Graduate and Postdoctoral Studies transcript team. Students should also keep the CAIML Graduate Program Coordinator informed if a hold may affect their ability to register.

  • Contact the CAIML Graduate Program Coordinator for questions about CAIML course registration, course planning, or Leave of Absence arrangements.

    When contacting the coordinator, include your UT EID in all communications.

Registration

Student Community

  • Students can connect with classmates through the Ed Discussions area of the CAIML Central course and in their individual Canvas courses. Discussion threads cover general CAIML topics and conversations tied to specific subject matter.

  • Yes. The CAIML Central course includes discussion threads for particular courses as well as general student discussions. You can use these to connect with other students, discuss the program, and exchange course-related information.

  • CAIML has a substantial and growing student community. Because enrollment changes each semester, students should refer to CAIML Central or current program information for the latest enrollment figures.

Cost & Scholarships

  • The program tuition is low-cost ($ 1,250 per course); CosmicAI will offer a small number of student fellowships.

  • Yes. See details below.

  • No. The CosmicAI scholarship does not require recipients to take a particular number of credit hours each semester. Scholarship recipients may take one course per semester and remain in good standing with both CAIML and the scholarship program.

  • Taking one course per semester is permitted under the current scholarship arrangement and does not, by itself, put a student out of good standing. Students should continue to meet CAIML academic requirements and any separately communicated CosmicAI scholarship requirements.

    If circumstances could substantially extend a student's expected completion timeline, the student should contact both CAIML and CosmicAI to confirm their completion plan.

  • For course selection, registration, academic requirements, Leaves of Absence, and CAIML policies, contact the CAIML Graduate Program Coordinator [aicertcoordinator@austin.utexas.edu].

    For questions specifically concerning CosmicAI scholarship funding, scholarship eligibility, or CosmicAI-specific requirements, contact the CosmicAI scholarship/program team [admin@cosmicai.org].

CosmicAI Scholarships

To support participation in this program, CosmicAI offers a tuition-free scholarship opportunity for junior faculty and early-career educators with a PhD to enroll in the AstroAI pathway within UT Austin’s Graduate Certificate in AI and Machine Learning (CAIML).

Eligibility Requirements

  • Must be Junior faculty or early-career educators

  • Research faculty, adjuncts, assistant, or associate professors

  • Astronomy, Physics, or a relevant academic career track

  • Must hold a PhD

Particularly encouraged: faculty at non-R1 or teaching-focused institutions

How to Apply

To be considered for scholarship support, applicants must complete both of the following:

Please note that scholarship support is contingent on meeting CAIML admission requirements and being admitted to the program.

For scholarship questions, contact the CosmicAI Program Coordinator, Alia Wofford.

Important Dates

Fall Application

Application Opens

December 15

Final Deadline

April 15

Decision Released

Rolling; latest by mid-December

Spring Application

Application Opens

June 1

Final Deadline

September 1

Decision Released

Rolling; latest by mid-June

Certificate questions: aicert@utexas.edu