Georgia Tech Machine Learning PhD

Last Updated on December 15, 2022

The doctoral program consists of additional coursework, programs of individual study, and research culminating in the dissertation. Most students entering the program with a bachelor’s degree require at least five years to complete a doctoral degree.

Do you want to gain information about Georgia Tech PhD programs? Have you been finding it difficult to know about Georgia Tech machine learning course? Are you interested in knowing about Georgia Tech machine learning master’s online? Do you want to obtain valuable information on Georgia Tech machine learning course or Georgia Tech PhD admission requirements? If yes, why worry yourself when Collegelearners provides information on topics like Georgia Tech PhD acceptance rate, and so much more.

PhD Machine Learning Online

The machine learning (ML) Ph.D. program is a collaborative venture between Georgia Tech’s colleges of Computing, Engineering, and Sciences. Approximately 25 students enter the program each year through eight different academic units. 

The central goal of the PhD program is to train students to perform original, independent research.  The most important part of the curriculum is the successful defense of a PhD Dissertation, which demonstrates this research ability.  The academic requirements are designed in service of this goal.

The curriculum for the PhD in Machine Learning is truly multidisciplinary, containing courses taught in eight schools across three colleges at Georgia Tech: the Schools of Computational Science and Engineering, Computer Science, and Interactive Computing in the College of Computing; the Schools of Aerospace Engineering, Biomedical Engineering, Electrical and Computer Engineering, and Industrial and Systems Engineering in the College of Engineering; and the School of Mathematics in the College of Science.

ML@GT manages all operations and curricular requirements for the new Ph.D. Program, which include four core and five elective courses, a qualifying exam, and a doctoral dissertation defense.

The curriculum for the PhD in Machine Learning is truly multidisciplinary, containing courses taught in eight schools across three colleges at Georgia Tech: the Schools of Computational Science and Engineering, Computer Science, and Interactive Computing in the College of Computing; the Schools of Industrial and Systems Engineering, Electrical and Computer Engineering, and Biomedical Engineering in the College of Engineering; and the School of Mathematics in the College of Science.

Students admitted into the ML Ph.D. program can be advised by any of our participating faculty. For those students looking to build a career in computing research, the School of Interactive Computing offers a range of Ph.D. programs that allow students to work side-by-side with some of the most brilliant researchers and computer scientists in the world.

For those looking to join the ranks of academia, we regularly place our doctorate graduates in tenure-track positions in top programs. The School maintains strong, research relationships with companies from Fortune 500 to the latest startups that allow graduates to continue their research in jobs at some of the world’s hottest private sector employers.

The Doctor of Philosophy with a major in Machine Learning program has the following principal objectives, each of which supports an aspect of the Institute’s mission:

  1. Create students that are able to advance the state of knowledge and practice in machine learning through innovative research contributions.
  2. Create students who are able to integrate and apply principles from computing, statistics, optimization, engineering, mathematics and science to innovate, and create machine learning models and apply them to solve important real-world data intensive problems.
  3. Create students who are able to participate in multidisciplinary teams that include individuals whose primary background is in statistics, optimization, engineering, mathematics and science.
  4. Provide a high quality education that prepares individuals for careers in industry, government (e.g., national laboratories), and academia, both in terms of knowledge, computational (e.g., software development) skills, and mathematical modeling skills.
  5. Foster multidisciplinary collaboration among researchers and educators in areas such as computer science, statistics, optimization, engineering, social science, and computational biology.
  6. Foster economic development in the state of Georgia.
  7. Advance Georgia Tech’s position of academic leadership by attracting high quality students who would not otherwise apply to Tech for graduate study.

Georgia Tech PhD Programs

Ph.D. in Computer Science

As a research-oriented degree, the Ph.D. in Computer Science prepares exceptional students for careers at the cutting edge of academia, industry and government. 

Ph.D. in Human-Centered Computing

A program devoted to the interdisciplinary science of designing computational artifacts that better support human endeavors.

Ph.D. in Robotics

Educating a new generation of robotics researchers prepared to be impactful contributors upon entering the high-tech workforce.

Ph.D. in Machine Learning

Machine learning builds and learns from both algorithm and theory to understand the world around us and create the tools we need and want.

Georgia Tech PhD Admission Requirements

The PhD in Machine Learning is an interdisciplinary doctoral program spanning three colleges (Computing, Engineering, Sciences).  Students are admitted through one of eight participating home schools:

  • Computer Science (Computing)
  • Computational Science and Engineering (Computing)
  • Interactive Computing (Computing)
  • Aerospace Engineering (Engineering)
  • Biomedical Engineering (Engineering)
  • Electrical and Computer Engineering (Engineering)
  • Industrial Systems Engineering (Engineering)
  • Mathematics (Sciences)

Applicants must meet all admissions standards (including requirements on the minimum GPA, minimum GRE/TOEFL scores) of the home unit, which may vary. After an initial review, the unit’s representative of the ML Ph.D. Faculty Advisory Committee (FAC) will submit their candidates for review and the final admission decision will be made by the ML FAC.

The committee’s decision to admit will be based on (1) prior academic performance of the applicant in a B.S. or M.S. program at a recognized institution, including coursework and independent research projects, (2) prior work experience relevant to ML, (3) the applicant’s statement of purpose, and (4) the letters of support.

machine learning PhD programs USA

At Georgia Tech, artificial intelligence (AI) and machine learning (ML) represent a large swath of faculty and research interests. We are concerned with constructing top-to-bottom and bottom-to-top models of human-level intelligence; building systems that can provide intelligent tutoring; creating adaptive and intelligent entertainment systems; making systems that understand their own behavior; growing our understanding of how to build autonomous agents that can adapt in dynamic environments involving multitudes of other intelligent agents, some of whom might be human; modeling and predicting human behavior; automating creativity; and addressing a variety of other problems. We advise Ph.D. and M.S. students in these areas, and we offer a broad set of undergraduate and graduate courses.

At the undergraduate level, AI and ML are mainly found in two threads: Intelligence and Devices. Commonly taken courses include Introduction to Artificial Intelligence, Machine Learning, Natural Language Understanding, Knowledge-based AI, Game AI and Pattern Recognition. Several courses in robotics and computational perception also have an AI or ML aspect. Versions of these courses are also available at the graduate level.

AI and machine learning often touch multiple areas and schools within the College of Computing, and different groups emphasize different aspects of the research. In the School of Interactive Computing, Ashok Goel, Charles Isbell, and Mark Riedl form a core of faculty, but many faculty in wearable computing as well as robotics and computational perception pursue related problems and apply similar techniques. There are also machine learning faculty in the schools of Computer Science and Computational Science & Engineering.

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