Frequently Asked Questions
The ML Ph.D. Handbook provides a detailed overview of the program and how it operates. Please see below for answers to our most commonly asked questions. If your question is not answered below or in the handbook, please contact the ML academic advisor, Stephanie Niebuhr at firstname.lastname@example.org.
General Program and Application Questions
Where can I apply for the ML Ph.D. program?
All application forms for graduate work at Georgia Tech are accepted through the admissions process which can be found here.
Do you have to be on campus to enroll in the program? Can Distance Learning students enroll in the program?
The Ph.D. program is only offered on-campus. You must apply to the Ph.D. program through one of our home units
Can any of the courses be done online?
Currently, none of the courses for the ML Ph.D. are offered online.
Do you have to have a master's degree to enroll in the program?
No master's degree is required. Most of our applicants are applying straight from their undergrad programs.
Is it possible to replace GRE test with GMAT?
No. The admissions requirements are determined by the home school through which you apply. Currently, all of the home schools have a minimum GRE and TOEFFL requirement
Can I get any prerequisite classes waived as my previous degrees were in?
There are no pre-requisite courses to apply to the program. Credit for individual classes towards the Ph.D. program is handled on a case-by-case basis by the ML academic advisor after a student has matriculated in the program.
What does it mean that the Machine Learning Ph.D. Program is a multidisciplinary program?
The Machine Learning Ph.D. Program is a collaboration of eight participating schools at Georgia Tech. Incoming Ph.D. students are admitted to the ML Ph.D. program through one of these home schools.
How is my application processed for the Machine Learning Ph.D. Program?
Your application is first processed in the home school. Application deadlines, minimum GRE/TOEFFL scores, and other requirements are all determined by the home school. Applications that satisfy all of the requirements are then forwarded to the Machine Learning Faculty Advisory Committee (ML FAC) for review. Decisions for admissions are made jointly between the home unit and the ML faculty.
Does the ML Ph.D. program offer support in the form of teaching assistantships, research assistantship, or fellowships?
Not directly. Teaching assistantships and fellowships are determined through the home schools. Research assistantships are typically funded through your thesis advisor but are subject to the rules imposed by the student's home school.
In addition, a student's home school may have extracurricular requirements (including a minimum number of semesters grading or serving as a TA). ML Ph.D. students are also subject to the extra-curricular requirements of their home schools.
Home Unit Questions
What home schools participate in the ML Ph.D. program?
Currently, there are 8 participating schools across 3 colleges:
Aerospace Engineering (College of Engineering) Biomedical Engineering (CoE) Computational Science and Engineering (College of Computing) Computer Science (CoC) Electrical and Computer Engineering (CoE) Industrial and Systems Engineering (CoE) Interactive Computing (CoC) Mathematics (College of Sciences)
Does it matter which home school I choose?
Yes. Home school may have different admissions requirements and deadlines. Additionally, for enrolled students, some home units may have GTA requirements, annual reviews, additional courses or seminars, and there may be differences in financial support. Please check with home schools for further details. The ML curriculum, qualifying exam, and thesis proposal, and defense requirements are the same for all ML students and can be found in the ML Handbook. Students are responsible for understanding and following both the ML program and their home schools’ policies.
How is a home unit selected on the application?
You will be asked to indicate a Program of Study on your application. Among the options are:
Ph.D. in Machine Learning (Aerospace Engineering) Ph.D. in Machine Learning (Biomedical Engineering), Ph.D. in Machine Learning (Electrical and Computer Engineering) Ph.D. in Machine Learning (Industrial and Systems Engineering) Ph.D. in Machine Learning (Mathematics),
Can an advisor from outside my home school serve as my thesis advisor?
Yes. Any faculty member affiliated with the ML Ph.D. program on this list can serve as your thesis advisor.
Are the curricular requirements different for the ML Ph.D. than for the Ph.D. program in the home school?
Yes. ML Ph.D. students have different course requirements and a different qualifying exam than the home school.
Detailed information about the ML Ph.D. curriculum can be found here.
Transfer Student Questions
I am currently a graduate student at Georgia Tech enrolled in a different degree program. Can I transfer into the ML PhD program?
Yes. Transfer applications are reviewed on a rolling basis. Potential transfer students will need to have found a thesis advisor who is willing to support them on a research assistantship. For more information, please email the ML Academic Advisor, Stephanie Niebuhr at email@example.com.