M.S. Degree in Artificial Intelligence (AI)


International Student Application

Domestic Student Application

The Masters in Artificial Intelligence degree prepares students with a focused  educational and research environment to develop career paths through necessary learning and training with emerging Artificial Intelligence technologies and applications to intelligent analytics, smart homes and communities, and robotics and automation.  Graduates will have technical knowledge and research and development skills necessary for applying artificial intelligence to industry, community, military including sectors requiring intelligent pattern-analysis of big data such as retail, healthcare, biology, psychology, and intelligent human-machine interactions and interfaces.

For more information on careers in AI, look here.

Admission Requirements:

Students entering the graduate program must have successfully completed high-level algebra, geometry, and calculus coursework (equivalent to the following Kent State courses: MATH 12002, MATH 12003, MATH 21001).  In addition, it is strongly recommended that students will have successfully completed coursework in computer programming, discrete structures, data structures and abstraction, operating systems (recommended but not mandatory), database, and computer algorithms equivalent to the following Kent State University courses: CS 13011, CS 13012, CS 23022, CS 23001, CS33211, CS 35101, CS 46101.  Highly qualified students lacking preparation in some standard areas may be considered for admission on a case-by-case basis.


  • Bachelor’s degree from an accredited college or university for unconditional admission
  • Minimum 3.000 undergraduate GPA (on a 4.000 point scale) for unconditional admission
  • Official transcript(s)
  • Two letters of recommendation
  • English language proficiency - all international students must provide proof of English language proficiency (unless they meet specific exceptions) by earning one of the following:
    • Minimum 525 TOEFL PBT score (paper-based version)
    • Minimum 71 TOEFL IBT score (Internet-based version)
    • Minimum 74 MELAB score
    • Minimum 6.0 IELTS score
    • Minimum 50 PTE score
    • Minimum 290 GRE score


For more information about graduate admissions, please visit the Graduate Studies website. For more information on international admission, visit the Office of Global Education website.

Program Learning Outcomes:

List the specific knowledge and skills directly related to the program’s discipline that you expect students will have at the time of graduation to be successful in the field. The outcomes must be observable and measureable, rather than what students “know,” “think,” “understand, “appreciate,” etc.


Graduates of this program will be able to perform one or more of the following tasks:

  1. Combine intelligent analytics and automation, human-computer interaction and robotics techniques to optimize and automate, transportation, industrial  process and/or healthcare processes.
  2. Apply machine learning techniques on big data to predict, classify, data mine   and explore patterns.
  3. Apply intelligent visualization and Internet-based techniques for smart homes and communities.
  4. Perform research, discovery and integration by applying knowledge of AI theory and techniques.


Program Requirements:


Major Requirements

Fundamental Courses (3 credit hrs. × Four mandatory core courses)  - Subtotal: 12 credits




CS 54201

Artificial Intelligence


CS 54202 

Principles of Machine Learning


CS 63005

Adv. Database Syst. Design


CS 64201

Adv. Artificial Intelligence


Foundational Course (One out of three courses)  Three credits

CS 53302

Algorithmic Robotics1


CS 64301

Pattern Recognition Principle1


CS 67302

Information Visualization


Electives (3 credit hrs. × three lecture courses) – Subtotal: 9 credits

CS 53301

Software Dev. For Robotics


CS 53303

Internet of Things


CS 53305

Advanced Digital Design


CS 53334

Human-Robot Interaction


CS 57201

Human Computer Interaction


CS 63015

Data Mining Techniques


CS 63016

Big Data Analytics


CS 63017

Big Data Management


CS 63018

Probabilistic Data Management


CS 63100

Computational Health Informatics


CS 63306

Embedded Computing


CS 64401

Image Processing and Vision


CS 64402

Multimedia System and Biometrics


CS 65203

Wireless and Mobile Communication


CS 67301

Scientific Visualization


Culminating Experience (six credit thesis) for Thesis Pathway OR

 [ (3-credit Capstone Project + 3 credit optional internship)   OR

Six-credit Capstone  Projects] for Non-thesis Pathway

CS 69099

Capstone Project  (non-thesis pathway)

3 or 6

CS 69192

Graduate Internship (optional, non-thesis pathway)


CS 69199

Thesis I (thesis pathway)


Minimum Total Credit Hours: