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Master of Professional Studies in
Artificial Intelligence

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Summary Courses Careers Costs and Aid

This 33-credit artificial intelligence master's degree will help you gain the skills and knowledge needed to develop intelligent systems and explore new frontiers in artificial intelligence, machine learning, deep learning, natural language processing, reinforcement learning, and computer vision.

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Complete your Penn State course work at your own pace and 100% online.

Apply by March 15 to start May 13

33 Credits $1,056 per credit

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Gain Skills to Excel in the Field of Artificial Intelligence

  • Form intelligent systems and explore new frontiers in artificial intelligence (AI) and machine learning (ML). 

  • Drive the design, development, and deployment of AI and ML products and services across a broad array of applications and industries. 

  • 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.

The Master of Professional Studies in Artificial Intelligence degree courses 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. 

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)

  • 3
    credits

    This course covers basic as well as advanced concepts to gain a detailed understanding of Natural Language Processing tasks such as language modeling, text to speech generation, natural language understanding, and natural language generation. Students can learn the necessary skills to design a range of applications, including sentiment analysis, translating between languages, and answering questions. The practical implementation of these applications with deep neural networks is also discussed.

    • Prerequisite

      STAT500 and A-I 570 or DAAN 570

  • 3
    credits

    This course will teach the foundations of AI and give students a practical understanding of the field. This course gives an overview of the core concepts of AI, including the intelligent agents, knowledge and reasoning, reinforcement learning, planning and acting, belief networks, computational learning, Markov decision process, and more.

  • 3
    credits

    The Ethics of Artificial Intelligence is the young branch of applied ethics that seeks to study the far-reaching and diverse ethical issues that arise with the widespread and rapid integration of AI technologies into various aspects of our lives.

  • 3
    credits

    This course focuses on the design of computer-based, machine vision systems using appropriate algorithms and best practices. Students will learn image representation and structuring; feature extraction and segmentation; and information extraction, filtering, and analysis.

  • 3
    credits

    This course will cover the foundations on neural networks and deep learning networks. It covers the core concepts of deep neural networks, including the convolutional neural networks for image recognition, recurrent neural networks for sequence generation, and generative adversarial networks for image generation.

    • Prerequisite

      STAT 500

  • 3
    credits

    This course will explore the development of analytics systems and the application of best practices and established software design principles using the Python programming language and its several toolkits.

  • 3
    credits

    Descriptive statistics, hypothesis testing, power, estimation, confidence intervals, regression, one- and two-way ANOVA, chi-square tests, diagnostics.

    • Prerequisite

      one undergraduate course in statistics

Electives (select 9 credits)

  • 3
    credits

    Creative projects, including nonthesis research, that are supervised on an individual basis, and which fall outside the scope of formal courses.

  • 3
    credits

    Formal courses given on a topical or special interest subject which may be offered infrequently.

  • 3
    credits

    This course will cover the main theory and approaches of reinforcement learning, along with deep learning and common software libraries and packages.

  • 3
    credits

    Examines tools and techniques required for data collection and computational procedures to automatically identify and eliminate errors in large data sets.

    • Prerequisite

      STAT 500

  • 3
    credits

    The objective of this course is to provide a foundation in the principles of network and predictive analytics along with hands-on experience with statistical analysis software for studying the interrelatedness of cyber-social and cyber-technical aspects of our society as a whole.

    • Prerequisite

      CSE 453 or IST 815

  • 3
    credits

    This course provides a foundation in the principles, concepts, techniques, and tools for visualizing large data sets.

  • 3
    credits

    The theory and application of several quantitative decision-making tools will be studied. The usefulness of these tools will be illustrated using projects and case studies throughout the course. Emphasis will be placed on the application of the tools and techniques and the results they generate.

    • Prerequisite

      STAT 500

  • 3
    credits

    Survey course on the key topics in predictive analytics. Students will learn methods associated with data analytics techniques and apply them to real examples using the R statistical system.

    • Prerequisite

      IE 323, STAT 500 or equivalent

  • 3
    credits

    Practical benefits of data mining will be presented; data warehousing, data cubes, and underlying algorithms used by data mining software.

    • Prerequisite

      INSC 521, or approval of instructor or department

Culminating Experience (3 credits)

  • 3
    credits

    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.

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.

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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.

Job Titles Related to This Degree

This degree program can prepare you for employment in multiple fields, including business, technology, finance, government, and manufacturing, holding titles such as:

  • Application Integration Engineer
  • Application Programmer Analyst
  • Artificial Intelligence Specialist
  • Data Scientist
  • Machine Learning Engineer
  • Software Development Engineer

Employment Outlook for Occupational Fields Related to This Degree

Estimates of employment growth and total employment are provided by the U.S. Bureau of Labor Statistics and are subject to change. While these occupations are often pursued by graduates with this degree, individual outcomes may vary depending on a variety of factors. Penn State World Campus cannot guarantee employment in a given occupation.

Software Developers

26%
employment growth (10 years)
1,534,790
total employment

Data Scientists

35.8%
employment growth (10 years)
159,630
total employment

Computer Programmers

-10.2%
employment growth (10 years)
132,740
total employment

Career Services to Set You Up for Success

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From the day you're accepted as a student, you can access resources and tools provided by Penn State World Campus Career Services to further your career. These resources are beneficial whether you're searching for a job or advancing in an established career.

  • Opportunities to connect with employers
  • Career counselor/coach support
  • Occupation and salary information
  • Internships
  • Graduate school resources 

Ready to Learn More?

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Ready to take the next step toward your Penn State master's degree?

Apply by March 15 to start May 13. How to Apply

Learn about this program's tuition, fees, scholarship opportunities, grants, payment options, and military benefits.

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.

2023–24 Academic Year Rates

Tuition rates for the fall 2023, spring 2024, and summer 2024 semesters.
How many credits do you plan to take per semester? Cost
11 or fewer $1,056 per credit
12 or more $12,678 per semester

2024–25 Academic Year Rates

Tuition rates for the fall 2024, spring 2025, and summer 2025 semesters.
How many credits do you plan to take per semester? Cost
11 or fewer $1,067 per credit
12 or more $12,805 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.

Penn State is an associate member of the Linux Foundation® (LF AI and Data Foundation). Working in tandem with services available through the Nittany AI Alliance, this membership allows students to expand their experiences with AI technologies by providing opportunities to learn from, contribute to, and interact with companies pursuing AI projects.

You can also join a faculty-led student group that focuses on contemporary issues in AI and coordinates student teams for AI/ML events, including the American Statistical Association DataFest, Kaggle competitions, and the Nittany AI Challenge.

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Whether you are looking to finish your program as quickly as possible or balance your studies with your busy life, Penn State World Campus can help you achieve your education goals. Many students take one or two courses per semester.

Our online courses typically follow a 12- to 15-week semester cycle, and there are three semesters per year (spring, summer, and fall). If you plan to take a heavy course load, you should expect your course work to be your primary focus and discuss your schedule with your academic adviser. 

To Finish Your Degree in One to Two Years

  • Take 3–4 courses each semester

To Finish Your Degree in Two to Three Years

  • Take 2–3 courses each semester 

To Finish Your Degree in Three to Four Years

  • Take 1 course each semester

Convenient Online Format

This program's convenient online format gives you the flexibility you need to study around your busy schedule. You can skip the lengthy commute without sacrificing the quality of your education and prepare yourself for more rewarding career opportunities without leaving your home.

A Trusted Leader in Online Education

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Penn State has a history of more than 100 years of distance education, and World Campus has been a leader in online learning for more than two decades. Our online learning environment offers the same quality education that our students experience on campus.

Information for Military and Veterans

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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.

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Apply by March 15 to start May 13

Application Instructions

Deadlines and Important Dates

Complete your application and submit all required materials by the appropriate deadline. Your deadline will depend on the semester you plan to start your courses.

  • Summer Deadline

    Apply by March 15 to start May 13

  • Fall Deadline

    Apply by July 15 to start August 26

  • Spring Deadline

    Apply by November 15, 2024, to start January 13, 2025

Steps to Apply

  1. 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. 

    Mathematics Requirement

    Applicants must complete Calculus I — equivalent to Penn State's MATH 140 — and 1 semester of probability or statistics.

    Programming 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.

  2. 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.

  3. To begin the online application, you will need a Penn State account.

    Create a New Penn State Account

    If you have any problems during this process, contact an admissions counselor at [email protected].

    Please note: Former Penn State students may not need to complete the admissions application or create a new Penn State account. Please visit our Returning Students page for instructions.

  4. 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.

    • 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.

  5. 5. Complete the application.

    The Graduate School Application

Admissions Help

If you have questions about the admissions process, contact an admissions counselor at [email protected].

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Have questions or want more information? We're happy to talk.

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 protected]

For general questions about Penn State World Campus:
World Campus Admissions Counselors
Phone: 814-863-5386
[email protected]

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 Penn State student on campus, and your courses will be taught by the same graduate faculty who are active in research and experts in their fields.

Faculty

  • Youakim Badr

    • H.D.R., University of Lyon
    • Ph.D., Computer Science, National Institute of Applied Sciences (INSA-Lyon)
    • M.S., Mathematical Modeling and Scientific Software Engineering, Francophone University Agency
    • M.S., Computer Science, Lebanese University
    • B.S., Computer Science, Lebanese University

    Dr. Youakim Badr, professor of data analytics and artificial intelligence, holds the position of professor-in-charge for the Master of Professional Studies in Artificial Intelligence program. His wide-ranging academic responsibilities encompass teaching a variety of courses, including deep learning, natural language processing, foundations of artificial intelligence, data mining, predictive analytics, statistics, and design and implementation of AI-based systems. Dr. Badr's research is primarily centered on the design and deployment of trustworthy AI service systems. Dr. Badr adopts a comprehensive and interdisciplinary approach, emphasizing AI analytics, trustworthy AI systems, and composable AI systems. He actively participates in various international conferences and contributes as a reviewer for national and international research funding programs. Furthermore, Dr. Badr is honored with a lifetime membership with ACM and holds an academic associate membership in the Linux Foundation for AI and Data (LFAI&Data).

  • Adrian S. Barb

    • Ph.D., Computer Science, University of Missouri
    • MBA, Finance and Management Information Systems, University of Missouri
    • B.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.

  • Phillip A. Laplante

    • Ph.D., Computer Science, Stevens Institute of Technology
    • M.B.A., University of Colorado
    • M.Eng., Electrical Engineering, Stevens Institute of Technology
    • B.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.

  • Ashkan Negahban

    • Ph.D., Industrial and Systems Engineering, Auburn University
    • M.E., Industrial and Systems Engineering, Auburn University
    • B.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.

  • Colin J. Neill

    • Ph.D., Software and Systems Engineering, University of Wales Swansea
    • M.Sc., Communications Systems, University of Wales Swansea
    • B.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.

  • Robin G. Qiu

    • Ph.D., Industrial Engineering, Penn State
    • Ph.D., (Minor), Computer Science, Penn State
    • M.S., Numerical Control, Beijing Institute of Technology, China
    • B.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.

  • Dusan Ramljak

    • Ph.D., Computer and Information Sciences, CST, Temple University
    • M.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.

  • Raghvinder S. Sangwan

    • Ph.D., Computer and Information Sciences, Temple University
    • M.S., Computer Science, West Chester University
    • B.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.

  • Hajime Shimao

    • Ph.D., Economics, Purdue University
    • M.S., Economics, Purdue University
    • M.S., Decision Science, Tokyo Institute of Technology
    • B.A., Psychology, University of Tokyo

    Dr. Hajime Shimao is an assistant professor of data analytics. He teaches courses in predictive analytics and data mining. His research applies machine learning and statistical techniques to a wide range of topics in interdisciplinary social science, including economics, sociology, law, and history. His articles have been published in top conferences, such as the International Conference on Machine Learning (ICML), and academic journals, such as Nature Communications.

  • Satish M. Srinivasan

    • Ph.D., Information Technology, University of Nebraska at Omaha
    • M.S., Industrial Engineering and Management, Indian Institute of Technology, Kharagpur
    • B.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.

  • Chengfei Wang

    • Ph.D., Computer Science, Auburn University
    • M.S., Computer Science, Auburn University
    • M.S., Biophysics, University of Electronic Science and Technology of China
    • B.S., Biotechnology, University of Electronic Science and Technology of China

    Dr. Chengfei Wang is an assistant professor of artificial intelligence. He teaches courses in foundations of AI and analytics programming in Python. His research interests include the robustness problem of deep learning models applied in life-critical missions and business intelligence based on natural language analysis of customer reviews on social media. His research on the robustness of the computer vision model was published at the Computer Vision and Pattern Recognition (CVPR) Conference.

Ready to take the next step toward your Penn State master's degree?

Apply by March 15 to start May 13. How to Apply

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