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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 nine 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, Chemical and Biomolecular 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.
Summary of ML PhD curriculum:
Core curriculum (4 courses, 12 hours). Machine Learning PhD students will be required to complete courses in four different areas: Mathematical Foundations, Intermediate Statistics, ML Theory and Methods, Data Models, and Optimization.
Area electives (5 courses, 15 hours). In addition to meeting the four core area requirements, each student is required to complete five elective courses. These courses are required for getting a complete breadth in ML. These courses must be chosen from at least two of the following five subject areas: Statistics and Applied Probability, Advanced Theory, Applications, Computing and Optimization, and Platforms. In addition, students can use up to six special problems research hours towards this requirement.
Responsible Conduct of Research (RCR) (1 course, 1 hour, pass/fail). Georgia Tech requires that all PhD students complete an RCR requirement that consists of an online component and in-person training. The online component is completed during the student’s first semester enrolled at Georgia Tech. The in-person training is satisfied by taking PHIL 6000 or their associated academic program’s in-house RCR course.
Qualifying examination (1 course, 3 hours). This consists of a one-semester independent literature review followed by an oral examination.
Doctoral minor (2 courses, 6 hours)* The minor follows the standard Georgia Tech requirement: 6 hours, preferably outside the student’s home unit, with a GPA in those graduate-level courses of at least 3.0. The courses for the minor should form a cohesive program of study outside the area of Machine Learning; no ML core or elective courses may be used to fulfill this requirement and must be approved by your thesis advisor and ML Academic Advisor. Typical programs will consist of two courses from the same school (any school at the Institute) or two courses from the same area of study. *Effective Summer 2023, new students entering the program will be required to take 2 minor courses, 6 hours. For students entering before summer 2023, the requirement is 3 courses, 9 hours.