Application deadline
Credits and costs
Nationally Recognized
Earn a Penn State Artificial Intelligence Master's Degree — 100% online
With recent advances in computing power and communications bandwidth and the ubiquity of internet-connected devices, artificial intelligence (AI) has emerged as an important discipline, driven heavily by the commercial availability of “smart” technologies and services. These advances correspond with the need for educated, qualified professionals who are capable of developing intelligent systems and exploring new frontiers in AI and machine learning (ML).
This online program is designed to provide technical education that empowers graduates to drive the design, development, and deployment of AI and ML products and services across a broad array of applications. Professionals working in the field of AI are responsible for identifying and acquiring relevant data sets, developing scalable algorithms based on state-of-the-art AI/ML (including deep learning), natural language processing, reinforcement learning, and computer vision. Their work also includes applying findings to smart consumer devices, medical imaging diagnostics, autonomous vehicles, and weapons systems.
Your Artificial Intelligence Online Courses
The Master of Professional Studies in Artificial Intelligence degree is a 33-credit online graduate degree program that can provide you with the skills and knowledge required to identify, acquire, process, and prepare relevant data sets; research, prototype, and develop algorithms to solve challenging computer vision, natural language, and multi-modal data-fusion tasks; and perform other emerging operations.
As a student in this program, you can learn how to:
- demonstrate interdisciplinary knowledge and comprehension of the major issues in AI and ML
- communicate the major issues of AI and its applications including theories, approaches, findings, and technical and ethical implications
- discriminate between state-of-the-art techniques in neural network architecture, machine learning, deep learning, and collective intelligence to determine the most appropriate approach for solving a given problem
Why Pursue a Penn State Degree Online?
Penn State World Campus offers a flexible, online platform that allows you to pursue a world-class education while you continue to gain valuable work experience. You will receive the same education as a resident Penn State student, and your courses will be taught by the same graduate faculty who are active in research and experts in their fields.
You can also join a faculty-led student group that focuses on contemporary issues in AI as well as coordinates student teams for AI/ML events including the American Statistical Association DataFest, Kaggle competitions, and the Nittany AI Challenge.
Penn State is an associate member of the Linux Foundation® (LF AI & Data Foundation). Working in tandem with services available through the Nittany AI Alliance, this membership allows students to expand their experiences with AI technologies with opportunities to learn from, contribute to, and interact with companies pursuing AI projects.
Information for Military and Veterans
Are you a member of the military, a veteran, or a military spouse? Please visit our military website for additional information regarding financial aid, transfer credits, and application instructions.
Courses
You must successfully completely a total of 33 credits at the 400, 500, or 800 level, while maintaining a GPA of 3.0 or better in all course work, including:
- at least 21 credits of required core courses at the 500 or 800 level, with at least 6 credits at the 500 level
- at least 9 credits of electives
- 3 credits of A-I 894, an integrative research topics course, which includes a written paper
Capstone Course
You will apply your knowledge of the theories, methods, processes, and tools of AI, learned throughout your program, in a culminating and summative experience. The choice of project topic and exact form will be mutually determined by you and your instructor. A written paper based on the applied project is required and must contain project description, analysis, and interpretation of findings.
Core Courses (21 credits)
Electives (select 9 credits)
Culminating Experience (3 credits)
Course Availability
If you're ready to see when your courses will be offered, visit our public LionPATH course search (opens in new window) to start planning ahead.
Costs and Financial Aid
Graduate Tuition
Graduate tuition is calculated based on the number of credits for which you register. Tuition is due shortly after each semester begins and rates are assessed every semester of enrollment.
| How many credits do you plan to take per semester? | Cost |
|---|---|
| 11 or fewer | $1,056 per credit |
| 12 or more | $12,672 per semester |
| How many credits do you plan to take per semester? | Cost |
|---|---|
| 11 or fewer | $1,067 per credit |
| 12 or more | $12,804 per semester |
Financial Aid and Military Benefits
Some students may qualify for financial aid. Take the time to research financial aid, scholarships, and payment options as you prepare to apply. Military service members, veterans, and their spouses or dependents should explore these potential military education benefits and financial aid opportunities, as well.
How to Apply
Deadlines and Important Dates
Your degree application, including receipt of all materials, must be received by the deadlines below to be considered complete. Space is limited, so you are encouraged to apply early.
To help you manage the application process, our online application management system will provide you with complete details regarding the required elements of your application portfolio — and will even help you track your progress. You can also save your work and return to complete your application at any time.
Admissions Help
Thank you for your interest in applying to this program. Contact an admissions counselor to discuss your educational goals, financial aid options, and application deadlines.
Admission Requirements
For admission to the Graduate School, an applicant must hold either (1) a baccalaureate degree from a regionally accredited U.S. institution or (2) a tertiary (postsecondary) degree that is deemed comparable to a four-year bachelor's degree from a regionally accredited U.S. institution. This degree must be from an officially recognized degree-granting institution in the country in which it operates.
Students should hold a bachelor's degree in computer science, engineering, or mathematics to be considered for admission to the program. Students from other disciplines will be considered based on prior course work (including the entrance requirements for mathematics and programming) and standardized test scores. Students should have earned at least a 3.00 junior/senior GPA (on a 4.00 scale) in their baccalaureate program.
Entrance to Major
Mathematics entrance requirement
Applicants must complete Calculus I equivalent to Penn State's MATH 140 and 1 semester of probability or statistics.
Programming entrance requirement
Applicants must complete two introductory-level programming courses where both courses used the same language. If an applicant believes his/her work experience satisfies the background, he/she should include a recommendation letter from a technical colleague describing the applicant’s coding contributions at work.
What You Need
Applications are submitted electronically and include a nonrefundable application fee. You will need to upload the following items as part of your application:
Official transcripts from each institution attended, regardless of the number of credits or semesters completed. Transcripts not in English must be accompanied by a certified translation. Penn State alumni do not need to request transcripts for credits earned at Penn State, but must list Penn State as part of your academic history. If you are admitted, you will be asked to send an additional official transcript. You will receive instructions at that time.
GPA — All applicants are expected to have earned a junior/senior grade-point average of 3.0 or higher.
GRE or GMAT — Scores are NOT required for admission.
English Proficiency — The language of instruction at Penn State is English. With some exceptions, international applicants must take and submit scores for the Test of English as a Foreign Language (TOEFL) or International English Language Testing System (IELTS). Minimum test scores and exceptions are found in the English Proficiency section on the Graduate School's "Requirements for Graduate Admission" page. Visit the TOEFL website for testing information. Penn State's institutional code is 2660.
References (2) — You will need to initiate the process through the online application by entering the names, email addresses, and mailing addresses of two references. Upon submission of your application, an email will be sent to each recommender requesting that they complete a brief online recommendation regarding your professional and/or academic strengths and accomplishments, and your potential for success in an online program. The admissions committee prefers that all recommendations be written within the last six months and reference the applicant's current career goals. Please inform your references that they must submit the form in order for your application to be complete.
Program-Specific Questions/Materials
Statement of Purpose — Provide a one-page written statement of intent, highlighting academic background, work experience, skills, strengths, academic interests, and professional goals.
Résumé — Upload your résumé to the online application.
Start Your Application
You can begin your online application at any time. Your progress within the online application system will be saved as you go, allowing you to return at any point as you gather additional information and required materials.
Begin the graduate school application
- Choose Enrollment Type: "Degree Admission"
- Choose "WORLD CAMPUS" as the campus
Checking Your Status
You can check the status of your application by using the same login information established for the online application form.
Technical Requirements
Given the scale of data used in the artificial intelligence program and the continuous advances in tools and platforms used in AI/ML, students are urged to check individual course technical requirements vigilantly. At a minimum, students will need a PC that runs Windows 10 or higher with 5GB of RAM and 250GB of free space on the hard drive. Mac OS machines are not compatible for several courses in the program and are not recommended.
Get the resources you need to make informed decisions about your education. Request information on this program and other programs of interest by completing this form.
Start or Advance Your Career
Start or Advance Your Career
You can use the knowledge gained from this program and the support of Penn State career resources to pursue careers in a variety of fields, depending on your goals.
Contact Us
To learn more about the Master of Professional Studies in Artificial Intelligence, offered in partnership with the Penn State Great Valley School of Graduate Professional Studies please contact:
For general questions about the program:
Dr. Amanda Neill
Email: [email protected]
For general questions about Penn State World Campus:
World Campus Admissions Counselors
Phone: 814-863-5386
Email: [email protected]
Faculty
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Youakim Badr
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DegreeH.D.R., University of Lyon
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DegreePh.D., Computer Science, National Institute of Applied Sciences (INSA-Lyon)
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DegreeM.S., Mathematical Modeling and Scientific Software Engineering, Francophone University Agenc
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DegreeM.S., Computer Science, Lebanese University
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DegreeB.S., Computer Science, Lebanese University
Dr. Youakim Badr, professor of data analytics, teaches courses in data mining, deep learning, predictive analytics, and analytics programming. Dr. Badr is interested in developing a new chain of data analytical models, tools, and platforms for designing and deploying trustworthy AI service systems by leveraging his research activities built around service computing and smart services for the IoT. He is a member of Linux Foundation for AI and Data (LFAI&Data).
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Adrian S. Barb
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DegreePh.D., Computer Science, University of Missouri
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DegreeMBA, Finance and Management Information Systems, University of Missouri
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DegreeB.S., Industrial Engineering, University of Bucharest
Dr. Adrian S. Barb, associate professor of information science, teaches databases, data mining, and big data courses. He has worked as a database programmer analyst as well as a web developer at University of Missouri. His research interests include data mining, knowledge discovery in databases, knowledge representation and exchange in content-based retrieval systems, semantic modeling and retrieval, conceptual change, ontology integration, and expert-in-the-loop knowledge generation and exchange.
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Phillip A. Laplante
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DegreePh.D., Computer Science, Stevens Institute of Technology
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DegreeM.B.A., University of Colorado
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DegreeM.Eng., Electrical Engineering, Stevens Institute of Technology
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DegreeB.S., Systems Planning and Management, Stevens Institute of Technology
Dr. Phillip A. Laplante, professor of software and systems engineering, pioneered the area of real-time image processing, co-founding the first journal and publishing the first two texts on the subject. For these achievements, he was named a Fellow of SPIE. In AI, he has investigated uncertain information processing using fuzzy sets and rough set theory and, more recently, has focused on the use of AI in safety-critical systems. He holds an appointment as a computer scientist in the Secure Systems and Applications group at the National Institute of Standards and Technology (NIST), working on the IoT, blockchain, and related technologies.
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Ashkan Negahban
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DegreePh.D., Industrial and Systems Engineering, Auburn University
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DegreeM.E., Industrial and Systems Engineering, Auburn University
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DegreeB.S., Industrial and Systems Engineering, University of Tehran
Dr. Ashkan Negahban, associate professor of engineering management, performs research on stochastic simulation, statistical data analysis, and optimization techniques that advances the science of decision-making in a wide range of applications, including manufacturing, sharing economy, and supply chains. He also conducts research on the use of machine learning (ML) in simulation models as well as training and testing ML/AI algorithms via simulations. His research has been supported by the NSF, Google, Microsoft, and multiple research institutes at Penn State.
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Colin J. Neill
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DegreePh.D., Software and Systems Engineering, University of Wales Swansea
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DegreeM.Sc., Communications Systems, University of Wales Swansea
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DegreeB.Eng., Electrical Engineering, University of Wales Swansea
Dr. Colin Neill is a professor of software engineering and systems engineering and the head of the MPS in Artificial Intelligence program. He has an extensive background in the design, architecture, and analysis of complex systems. His AI–related work includes industrial applications of machine vision and expert systems; applications of fuzzy sets and rough set theory to uncertainty in software engineering; individual and team cognition processes; network analytics; and text mining and natural language processing of social media.
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Robin G. Qiu
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DegreePh.D., Industrial Engineering, Penn State
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DegreePh.D., (Minor), Computer Science, Penn State
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DegreeM.S., Numerical Control, Beijing Institute of Technology, China
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DegreeB.S., Mechanical Engineering, Beijing Institute of Technology, China
Dr. Robin G. Qiu is a professor of information science. He teaches courses on data analytics, information science, software engineering, and cyber security. His research includes data and computational sciences, health-care analytics, smart service systems (health care, city mobility, energy efficiency, IoT, etc.), blockchain, and cybersecurity analytics. He served as the editor-in-chief of INFORMS Service Science and as an associate editor of IEEE Transactions on Systems, Man, and Cybernetics and IEEE Transactions on Industrial Informatics and has more than 170 publications.
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Dusan Ramljak
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DegreePh.D., Computer and Information Sciences, CST, Temple University
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DegreeM.Sc. and B.Sc., Electrical Engineering - Systems Control, University of Belgrade, Serbia
Dr. Dusan Ramljak, assistant teaching professor of information science, teaches courses on information science, data science, storage systems, and emerging technologies. He has been applying data science on storage systems in NSF IUCRC projects with HPE, Dell, Huawei, and other companies and has more than 20 years of system administration experience facilitating business and research in the U.S., Portugal, and Serbia. His research interests include solving challenging storage systems, provenance, and caching problems, and developing and integrating distributed and parallel data mining and statistical learning technology for an efficient knowledge discovery at large sequence and temporal databases.
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Raghvinder S. Sangwan
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DegreePh.D., Computer and Information Sciences, Temple University
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DegreeM.S., Computer Science, West Chester University
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DegreeB.S., Genetics and Plant Breeding, Haryana Agricultural University
Dr. Raghvinder S. Sangwan is a professor of software engineering and director of the Big Data Lab — a research collaborative focused on data science and artificial intelligence and their applications. His own research in this space focuses on network analytics approaches to large-scale complex systems, explainable AI, and the design of secure AI systems.
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Satish M. Srinivasan
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DegreePh.D., Information Technology, University of Nebraska at Omaha
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DegreeM.S., Industrial Engineering and Management, Indian Institute of Technology, Kharagpur
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DegreeB.S., Information Technology, Bharathidasan University
Dr. Satish Srinivasan is an associate professor of information science. He teaches courses in data retrieval, processing, storage, and mining; predictive and prescriptive analytics; and the application of analytics to particular domains, including cybersecurity and sport. His research interests include natural language processing and text mining of social media; network analytics techniques to determine critical elements in large-scale networks; and the application of machine learning to bioinformatics and genomics.
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