Doctor of Philosophy in Artificial Intelligence

Admission Requirements

  1. Master’s degree, normally in a computing, engineering, or science related field or a Bachelor’s degree, normally in a computing, engineering, or science related field.
  2. An overall GPA of at least 3.0 (on a 4.0 scale) for the bachelor's degree, and master's if applicable.
  3. Satisfy the School of Graduate Studies’ English Language Proficiency requirements as listed in the Graduate Academic Information section.

The School of Electrical Engineering and Computer Science recognizes that the prerequisite expertise identified above may be acquired in a variety of ways. Students who do not meet all of the requirements may be admitted with provisional status with the obligation to meet the remaining requirements early in their graduate study.

Degree Requirements

Students seeking the Doctor of Philosophy in Computer Science degree must satisfy all general requirements set forth by the School of Graduate Studies. In addition, they must meet the following requirements set by the School of Electrical Engineering and Computer Science:

  1. Completion of 90 credit hours beyond the bachelor's degree.
  2. Maintain a GPA of at least 3.0 for all classes completed as a graduate student.

Requirements for Students with an Approved Master's Degree

Master's degree must be in an approved topic related to computing.

  1. Complete 15 credit hours from the list of Core Required Courses
  2. Complete 6 credit hours in each of 3 groups of elective courses (18 credit hours total)
  3. Complete at least 6 credit hours of EECS 510 Artificial Intelligence Seminar
  4. Complete 21 credit hours of Dissertation Research
  5. Successfully complete the Qualifying Examination consisting of both the written proposal and an oral defense. 
  6. Oral Final Examination which includes a defense of the student’s dissertation.  The oral defense of the student’s dissertation must take place at least one semester after satisfactory completion of the comprehensive examination.
  7. Submission of the dissertation document, approved by the student’s Faculty Advisory Committee.

Requirements for Students with an Approved Bachelor Degree

  1. Complete 9 credit hours of Background Courses
  2. Complete 15 credit hours from the list of Core Required Courses
  3. Complete 9 credit hours in each of 3 groups of elective courses (27 credit hours total)
  4. Complete at least 9 credit hours of EECS 510 Artificial Intelligence Seminar
  5. Complete 30 credit hours of Dissertation Research
  6. Successfully complete the Qualifying Examination consisting of both the written proposal and an oral defense. 
  7. Oral Final Examination which includes a defense of the student’s dissertation.  The oral defense of the student’s dissertation must take place at least one semester after satisfactory completion of the comprehensive examination.
  8. Submission of the dissertation document, approved by the student’s Faculty Advisory Committee.

Background Courses

DATA 5113
DATA 5123
DATA 5133

Core Required Courses

DATA 530Artificial Intelligence3
DATA 532Applied Machine Learning3
PHIL 570Philosophical and Ethical Implications of AI and Emerging Technologies3
DATA 541
CSCI 548

Elective Group 1: AI Foundations

CSCI 575Analysis of Algorithms3
PSYC 532Cognitive and Behavioral Foundations in AI3
POLS 504
COMM 406Future of Communication Technology3
SOC 460Technology and Society (Technology and Society)3
HIST 530History of Technology (History of Technology)3
PSYC 539Cognitive Psychology3

Elective Group 2: Advanced AI Techniques

CSCI 543Machine Learning3
CSCI 544Soft Computing: Computational Intelligence I3
CSCI 554Applications in AI/Computational Intelligence3
DATA 525Data Engineering and Mining3
DATA 527Predictive Modeling3
DATA 540Data Visualization3
CSCI 542
CSCI 549

Elective Group 3: Machine Vision and Robotics

EE 751Wireless Sensor Networks3
EE 752Introduction to Autonomous Systems3
ME 580Introduction to Autonomous Robotics3
ME 566Introduction to Machine Vision3
EE 563Digital Image Processing3
EE 557Robotics Fundamentals3

Elective Group 4: AI Applications

BIMD 514Foundations of Bioinformatics3
CHEM 534Quantum and Computational Chemistry3
COMM 522Data Mining Analytics for Communication Professionals3
EFR 530Learning Analytics3
COMM 549Information Communication Technologies3
EFR 535Data Analytics and Visualization with R3
PSYC 533Theories of Learning3
PSYC 540Foundations of Behavioral Data Analytics3
PHIL 575Data Science Ethics3
PSYC 537Physiology of Behavior and Psychophysiological Measurement3