Artificial Intelligence, M.S.
Benjamin M. Statler College of Engineering and Mineral Resources
The Master of Science in Artificial Intelligence from WVU's Benjamin M. Statler College of Engineering and Mineral Resources, is a fully online graduate program designed to prepare you for real-world, industry-ready roles in today’s rapidly evolving AI landscape. Tailored for professionals and recent graduates with foundational knowledge or practical experience in computer science or related fields, the program blends theoretical foundations with applied skills in machine learning, intelligent systems, and data-driven decision-making.
Cost:
$785 / Cost per credit hour *
Courses and Credits:
10 Courses / 30 Credit Hours
Duration:
One to three years / 8-week terms
Dates:
Next Start: January 12, 2026 Admission Terms: Fall, Spring, Summer
Learning Format:
Asynchronous with synchronous office hours
Program Overview
Shape The Future
The M.S. in Artificial Intelligence from Statler's Lane Department of Computer Science and Electrical Engineering is a fully online graduate program designed for professionals with a background in computer science or related fields. This highly flexible and customizable program allows you to tailor the classes you take to your interest. The curriculum blends theory with hands-on application, equipping you with the skills to design, implement, and evaluate AI solutions across industries like healthcare, cybersecurity, energy, and intelligent automation.
- Comprehensive Curriculum: Complete courses tailored to your interests in machine learning, intelligent systems, and data-driven decision-making.
- Applied AI Skills: Gain the ability to develop and test AI techniques across real-world domains and emerging technologies.
- Research and Communication: Learn to conduct AI research and effectively communicate findings to technical and non-technical audiences.
- Ethical Awareness: Understand the societal impact of AI, including issues of bias, fairness, and accountability.
- Flexible Format: Designed for working professionals, career changers, and aspiring researchers, with options to graduate in one to three years.
Curriculum
Learning Outcomes
- Have knowledge of the general concepts and principles of artificial intelligence, and the various areas of artificial intelligence technologies and methodologies.
- Understand the applicability of artificial intelligence technologies and methodologies across a range of problem domains.
- Develop new artificial intelligence techniques, and to apply principles and methods of AI to a variety of application domains.
- Engage in artificial intelligence research, to interpret the findings of artificial intelligence research, and to communicate findings to technical and non-technical audiences.
- Apply artificial intelligence methodologies to address issues in a professional or academic area of interest.
- Understand the ethical and societal implications of artificial intelligence development and deployment in modern society.
- CS 560: Big Data Engineering, 3 credit hours
- CS 574: Responsible and Safe AI, 3 credit hours
- CS 677: Pattern Recognition, 3 credit hours
- CS 676: Machine Learning, 3 credit hours
- CPE 520: Application of Neural Networks, 3 credit hours
- CPE 553: Advanced Networking Concepts, 3 credit hours
- CPE 620: Deep Learning, 3 credit hours
- CS 460: Introduction to Big Data Engineering, 3 credit hours
- CS 472: Artificial Intelligence, 3 credit hours
- CS 573: Advanced Data Mining, 3 credit hours
- CS 678: Computer Vision, 3 credit hours
- CS 693: Special Topics*, 3 credit hours
- CYBE 564: Software Engineering of Mobile Applications, 3 credit hours
- or SENG 564: Software Engineering of Mobile Applications, 3 credit hours
- CYBE 650: Cloud Computing for the Internet of Things, 3 credit hours
- or SENG 650: Cloud Computing for the Internet of Things, 3 credit hours
- EE 513: Stochastic Systems Theory, 3 credit hours
- EE 465: Introduction to Digital Image Processing, 3 credit hours
- EE 565: Advanced Image Processing, 3 credit hours
- EE 668: Information Theory, 3 credit hours
Connect With Us
Dr. Donald Adjeroh Benjamin M. Statler College of Engineering and Mineral Resources
304-293-9681 Donald.Adjeroh@mail.wvu.edu
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Admissions Process and How to Apply
- Submit a personal statement
- Your personal statement should be 750 to 1,000 words and double-spaced.
- This is an opportunity to tell the admissions committee more about your reasons to earn this degree and should not repeat the information on your resume.
- Submit two (2) professional and/or educational references contact information only.
- Submit official transcripts showing degree completion of a bachelor’s degree in computer science, computer engineering, cybersecurity, or a closely related field from an accredited University, with a minimum cumulative grade point average of 3.0 (on a 4-point scale) or better.
- Students with a degree in other fields of study from accredited institutions, but having at least one year experience in cybersecurity/AI/ML may be considered for provisional admission.
- One year experience should be highlighted through reference letter(s) and other documents. Provisional students will be required to complete three core courses with a ‘B’ or above. After successful completion of three core courses, the student will move to regular graduate status.
- Submit a resume that reflects your education and experience.
- International applicants must meet the WVU requirement of English language proficiency.
Tuition, Fees and Financial Aid
- File the application for Federal Student Aid (FAFSA) by the June deadline to determine eligibility for funding and scholarships.
- Inquire with your employer about potential financial support for continuing your education.
Careers and Employment
After completing this program, you will have the skills, knowledge, and perspective to succeed in a variety of roles across different industries.
Career Paths
- AI/ML Engineer
- Data Scientist
- AI Researcher
- Scientist
- AI Product Manager
- AI Analyst
- Entrepreneur