Artificial intelligence and related technologies like machine learning, image processing, and natural language processing are important components of today’s business, entertainment, media, and social interaction arenas — and they’ll be crucial for the future. But some experts predict there will be a talent shortage of AI professionals even as demand skyrockets in the coming years.*
If you want to build a high-tech career in AI, an advanced degree such as a Master of Science in Artificial Intelligence can help you build the knowledge, skills, and experience you need to brave this vital field and take advantage of employer demand — while helping meet tomorrow’s challenges.
*The Hill, “The reality of America’s AI talent shortages”
What can you expect from Maryville University’s online MS in Artificial Intelligence curriculum?
At Maryville University, we built our 100% online master’s degree in artificial intelligence curriculum with your future in mind. Featuring two built-in professional certificates, our program gives you the opportunity to learn from experts in the field and benefit from experiential, project-based education that connects what you study in the classroom with what you’ll do in your career. Study online, on your time, from wherever is convenient for you, and prepare to lead and thrive in the booming AI field.
Core Courses
| MATH 509 | Mathematics for Artificial Intelligence | 3 Credits |
This course provides you with the necessary mathematical background to understand algorithms encountered in machine learning, artificial intelligence, and related fields. Topics covered include probability theory, statistics, calculus, linear algebra, and optimization. | ||
| DSCI 503 | Python | 3 Credits |
This course covers data types, statements, expressions, control flow, top Python core libraries (NumPy, SciPy, Pandas, Matplotlib, and Seaborn) and modeling libraries (Statsmodels and Scikit-learn). Project-based learning is used to help students develop effective problem solving and collaboration skills. Note: This course is for graduate students only. Related Courses: DSCI 303 | ||
| DSCI 508 | Machine Learning | 3 Credits |
This course provides an introduction to machine learning. Topics include supervised learning, machine learning algorithms, learning theory, reinforcement learning and adaptive control, neural networks, and applications of machine learning to data mining, autonomous navigation, and web data processing. Related Courses:DSCI-408 Prerequisite:DSCI-503 | ||
| DSCI 619 | Deep Learning | 3 Credits |
This course is an introduction to deep learning with an emphasis on the development and application of advanced neural networks. It covers convolutional neural networks, recurrent neural networks, generative adversarial networks, and deep reinforcement learning. Project-based learning is used to help students develop effective problem solving and collaboration skills. Related Courses: DSCI-419 Prerequisite: DSCI-508 | ||
| COSC 635 | Reinforcement Learning | 3 Credits |
This course provides an introduction to the field of Reinforcement Learning, with an emphasis on Deep Reinforcement Learning. Topics cover may include, but are not limited to: Markov decision processes, value-based methods, Deep Q-networks, policy-gradient methods, actor-critic algorithms, and multi-agent problems. Prerequisite: DSCI 508 | ||
| COSC 640 | Fundamentals of Artificial Intelligence | 3 Credits |
This course provides an introduction to the field of Artificial Intelligence. Topics covered may include, but are not limited to, History of Artificial Intelligence, logic, game theory, search algorithms, knowledge representation, and automated planning. Prerequisite: DSCI 503 | ||
| COSC 643 | Ethics of Artificial Intelligence | 3 Credits |
This course introduces you to a range of potential ethical issues related to the current and future use of artificial intelligence. Topics include the role of artificial intelligence in society, as well as the use of artificial intelligence in areas such as manufacturing, finance, healthcare, government, and law enforcement. | ||
| DSCI 598 | Capstone Project | 3 Credits |
The Capstone Project course is for the students to apply the knowledge acquired during the Data Science program to a company project involving actual data in a realistic setting. Students will engage in the entire process of solving a real-world data science project, from collecting and processing actual data to applying suitable and appropriate analytic methods to the problem. | ||
Elective Courses
Choose any 3 courses
| DSCI 614 | Text Mining | 3 Credits |
This course covers text analytics, the practice of extracting useful information hidden in unstructured text such as social media, emails, and web pages using Python. Topics include working with corpora, transformations, metadata management, term document matrices, word clouds, and topic models. Project-based learning is used to help students develop effective problem solving and collaboration skills. Related Courses: DSCI-314 Prerequisite: DSCI-508 | ||
| COSC 521 | Robotics | 3 Credits |
This course provides an introduction to robotics, applications of robots, return-on-investment, abstract models, controlling robot motion, complex motion, robotic sensors, input/output, external sensors, threads, event programming, remote communication, remote sensing, behavior programming, and human/robot interfaces. Students will gain hands-on experience with emerging robot technologies, understand industrial applications of robots, and ramifications of human/robot interaction. Prerequisite: DSCI 503 | ||
| COSC 523 | Image Processing | 3 Credits |
This course covers methods that allow a machine to analyze, learn from, and make images from image and video data. It covers a range of image processing techniques and applications, such as image sampling, noise reduction, transformation, feature extraction, image classification, image segmentation, and keypoint detection. Prerequisite: DSCI 503 | ||
| COSC 641 | Advanced Artificial Intelligence | 3 Credits |
This course continue the study of the field of Artificial Intelligence. Topics covered may include, but are not limited to: Markov decision processes, evolutionary algorithms, probabilistic reasoning, multi-agent problem-solving, natural language processing, computer vision, and robotics. Prerequisite: COSC 640 | ||
| COSC 645 | Applications of Artificial Intelligence | 3 Credits |
This course is a survey of some of the current and possible future applications of artificial intelligence. The course will explore applications in fields such as business, transportation, manufacturing, healthcare, cybersecurity, and geospatial analysis. | ||
To ensure the best possible educational experience for our students, we may update our curriculum to reflect emerging and changing employer and industry trends.
Ready for your next step?
What are some common concepts, skills, and competencies covered in an MS in Artificial Intelligence curriculum?
Full master’s degrees in artificial intelligence are still new and innovative. At Maryville University, we have a reputation for being future-focused and tech-forward, and our master’s in AI is one of the only fully online experiences of its kind in the U.S.
Some of the skills you can expect to cover in your master’s in artificial intelligence curriculum may include:
Working with state-of-the-art AI technology.
Advanced artificial intelligence degree programs typically focus on using techniques and technology related to AI, deep learning, natural language processing, big data, computational linguistics, and machine vision. In addition, learners typically work with computer programming languages like R, Python, and C++, along with cloud services such as AWS and Google Colaboratory.
Want to learn more about some of this technology? Maryville University also offers 100% online professional certificates in areas like big data and machine learning that can give you a streamlined pathway to building graduate-level understanding in these fields.
Understanding applications and uses of AI.
Artificial intelligence has an incredible number of uses and applications in a wide range of professional fields. The metaverse, the “internet of things,” augmented and virtual reality, automation, driverless cars, robotics, and security are just a few areas where AI stands to revolutionize operations. Your master’s degree can help you learn how best to use these techniques and technologies for today’s business, entertainment, social interaction, and media needs — and to prepare for future innovations.
Building independent research and critical thinking skills.
Technology develops and evolves fast. When you enter a field like AI, you’ll need to be prepared to keep up or stay ahead of trends and technologies. Your graduate degree in artificial intelligence should help you develop your research skills and hone your critical thinking abilities, so you can keep growing your expertise throughout your career.
Practicing ethical stewardship of AI tech.
One of the trends shaping the future of artificial intelligence is ethical and responsible practice. The technological research firm Gartner suggests that most or all AI professionals will be required to have training in ethics as early as 2023.* AI and related technologies are powerful tools that can be used to change and improve many aspects of society, but it’s imperative to keep an eye toward transparency, auditability, limiting bias, and establishing trust. Your master’s degree should feature discussions and lessons in these topics to prepare you for conscientious stewardship of AI and related technologies.
*Gartner, “Gartner Identifies Four Trends Driving Near-Term Artificial Intelligence Innovation”
What are some common master’s in artificial intelligence courses?
An advanced, graduate-level artificial intelligence curriculum can provide you with practical skills that prepare you to develop and operate state-of-the-art technology in AI and related fields like machine learning and image processing. Coursework typically includes core AI and computer science classes to help build your foundation and advanced skills, as well as electives to help you to tailor your educational experience to your career goals. Some of these courses include:
Deep Learning/Deep Reinforcement Learning.
Deep learning and deep reinforcement learning are related but distinct topics in AI that are typically covered separately. The Deep Learning class should cover advanced artificial neural networks, touching on topics like recurrent neural networks, generative adversarial networks, Hopfield networks, and Boltzmann machines. The Deep Reinforcement Learning course covers areas like Markov decision processes, value-based methods, Deep Q-networks, policy-gradient methods, actor-critic algorithms, and multi-agent problems.
Text Mining.
One area in which artificial intelligence can drastically improve efficiency and comprehension is text mining. Courses in text mining can teach you how to use coding languages like R and Python to comb through unstructured text and glean new insights from social media, emails, and websites. Topics covered include corpus, transformations, metadata management, term document matrix, world cloud, and topic models.
Robotics.
The field of robotics is tied heavily with artificial intelligence technology, and courses in robotics can help you study and understand how that relationship operates and how robotics can be used to enhance business and organizational practices. These courses also cover complex topics like abstract models, complex motions, external and remote sensors, robotics behavior, event programming, and human-robot interaction.
Image Processing.
With artificial intelligence, computers can study and analyze imagery and video footage to discover new insights and learn new information. Image processing classes typically cover visual-focused topics like image sampling, noise reduction, classification and segmentation, feature extraction, and keypoint detection.
Fundamental and Advanced AI.
While other classes in your graduate-level AI curriculum feature deep dives into specific topics, classes in fundamental and advanced AI help you develop a broad understanding of the field. Courses in AI fundamentals may give you historical and technological context and touch on areas like algorithms, game theory, logic, knowledge representation, and automated planning. Then, courses focused on advanced AI plunge deeper into complex topics like evolutionary algorithms, probabilistic reasoning, natural language processing, computer vision, and robotics.
Learn more about the MS in Artificial Intelligence curriculum
If you’re excited by the endless possibilities AI presents for your career, consider an online Master of Science from Maryville University.
You can earn this advanced degree entirely through Canvas, our intuitive online learning platform, and build valuable tools that can help you navigate the AI careers of today — and roles that don’t even exist yet. When you choose to pursue your master’s in artificial intelligence, you can build the skills to seek employment in a wide range of fields and industries.
Take the next step toward a rewarding future and see what our online MS in Artificial Intelligence can do for you.