M.S. Degree in Artificial Intelligence (AI)
AVAILABLE IN FALL 2021
International 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:
- Combine intelligent analytics and automation, human-computer interaction and robotics techniques to optimize and automate, transportation, industrial process and/or healthcare processes.
- Apply machine learning techniques on big data to predict, classify, data mine and explore patterns.
- Apply intelligent visualization and Internet-based techniques for smart homes and communities.
- Perform research, discovery and integration by applying knowledge of AI theory and techniques.
Program Requirements:
Major Requirements |
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Fundamental Courses (3 credit hrs. × Four mandatory core courses) - Subtotal: 12 credits |
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Course |
Title |
Credits |
CS 54201 |
Artificial Intelligence |
3 |
CS 54202 |
Principles of Machine Learning |
3 |
CS 63005 |
Adv. Database Syst. Design |
3 |
CS 64201 |
Adv. Artificial Intelligence |
3 |
Foundational Course (One out of three courses) Three credits |
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CS 53302 |
Algorithmic Robotics1 |
3 |
CS 64301 |
Pattern Recognition Principle1 |
3 |
CS 67302 |
Information Visualization |
3 |
Electives (3 credit hrs. × three lecture courses) – Subtotal: 9 credits |
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CS 53301 |
Software Dev. For Robotics |
3 |
CS 53303 |
Internet of Things |
3 |
CS 53305 |
Advanced Digital Design |
3 |
CS 53334 |
Human-Robot Interaction |
3 |
CS 57201 |
Human Computer Interaction |
3 |
CS 63015 |
Data Mining Techniques |
3 |
CS 63016 |
Big Data Analytics |
3 |
CS 63017 |
Big Data Management |
3 |
CS 63018 |
Probabilistic Data Management |
3 |
CS 63100 |
Computational Health Informatics |
3 |
CS 63306 |
Embedded Computing |
3 |
CS 64401 |
Image Processing and Vision |
3 |
CS 64402 |
Multimedia System and Biometrics |
3 |
CS 65203 |
Wireless and Mobile Communication |
3 |
CS 67301 |
Scientific Visualization |
3 |
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 |
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CS 69099 |
Capstone Project (non-thesis pathway) |
3 or 6 |
CS 69192 |
Graduate Internship (optional, non-thesis pathway) |
3 |
CS 69199 |
Thesis I (thesis pathway) |
6 |
Minimum Total Credit Hours: |
30 |