M.S. Degree in Artificial Intelligence (AI)

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The Masters in Artificial Intelligence degree is a STEM area that prepares students with a focused  educational and research environment to develop career paths through necessary learning and training with emerging Artificial Intelligence technologies (including machine learning) 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 and visualisation of big data such as retail, healthcare, biology, psychology, and intelligent human-machine interactions and interfaces.  There is a strong growing industrial demand for AI graduates with starting salary averaging around US$ 120,000 per year.

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Admission Requirements:

Students entering the graduate program must have successfully completed high-level algebra, geometry, and calculus coursework (equivalent to the following Kent State math courses: MATH 12002, MATH 12003, MATH 21001) with a grade of B or above.  In addition, it is strongly recommended that students will have successfully completed computer science coursework with a grade of B or above in computer programming, discrete structures, data structures and abstraction, database systems, operating systems (preferred but not mandatory), and computer algorithms equivalent to the following Kent State University cs courses: CS 13011, CS 13012, CS 23022, CS 23001, CS33007, CS33211,  and CS 46101.  Admission to this interdisciplinary program is holistic.  Highly qualified students from related disciplines, 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 University approved GPA for admission (2.75 on a scale of 4.0 for Fall 2023)  is necessary for unconditional admission. A GPA of 3.0 (on a scale of 4.0) or above is strongly recommended.  Students having a GPA between 2.75 - 3.00 may be considered on a case-by-case basis depending upon their performance in recommended courses and/or AI related background preparation.
  • 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
  • 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:

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

Fundamental Courses (3 credit hrs. × Four mandatory core courses)  - Subtotal: 12 credits1

Course

Title

Credits

CS 54201

Artificial Intelligence

3

CS 54202 

Machine Learning and Deep Learning

3

CS 63005

Advanced Database System Design

3

CS 64201

Advanced Artificial Intelligence

3

Foundational Course (One out of three courses)  Three credits

CS 53302

Algorithmic Robotics

3

CS 64301

Pattern Recognition Principles

3

CS 67302

Information Visualization

3

Electives (3 credit hrs. × three lecture courses) – Subtotal: 9 credits2

CS 53301

Software Development 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

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

It is strongly recommended to take fundamental courses first, before taking elective courses.

2 In addition to above electives, new special topic graduate-level courses in the areas of machine learning, natural language processing, computer vision, and information visualization are offered in the CS Department and can be taken as elective with the permission of the AI program director.  The lists of the such courses are announced in the beginning of every semester. 

Artificial Intelligence Faculty:

AI-related courses are taught by accomplished PhD Computer Science professors.  Currently, the AI-related professors are Arvind Bansal, Michael Carl, Qiang Guan, Ruoming Jin, Javed Khan, Jong-Hoon Kim, Jungyoon Kim, Kwangtaek Kim, Xiang Lian, Cheng Chang Lu, Hassan Peyravi, Augustine Samba, Gokarna Sharma, and Ye Zhao.  The details and personal websites of the faculty members are available here. The professors have their research laboratories where students can finish their final projects/thesis.  Students will also be able to learn multiple projects through AI projects lab which will be equipped with many AI software tools, resources and related projects.  

The curriculum is continuously monitored and upgraded, based on industry requirements, by a committee of three professors: Arvind Bansal (director of the program and analytics theme representative), Jong-Hoon Kim (member and robotics and automation themes representative), and Ye Zhao (member and visualization theme representative).  

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