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Artificial Intelligence Master's
Master of Science (M.S.)
Program type:
Major
Format:
On Campus
Est. time to complete:
2-3 years
Credit Hours:
33
Help develop the next great technological innovation.
From streaming service recommendations to self-driving cars, artificial intelligence
is playing a bigger role in our everyday lives — now more than ever. Get the training
you need to contribute to technology's next leap forward.
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Request More InfoWhy Earn a degree in Machine Learning Artificial Intelligence?
UNT’s Artificial Intelligence master's degree will provide the skills and knowledge you need to qualify for jobs in the desirable and up-and-coming field of artificial intelligence. This is a STEM-designated master's program with concentrations in Machine Learning, Biomedical Engineering, and Autonomous Systems.
Students who graduate from this program will be able to:
- Understand and apply the concepts of programming related to AI
- Identify and implement AI applications to solve problems
- Create and utilize AI systems that respond to market needs
- Analyze and apply AI concepts and applications available in their chosen field of interest
- Understand the business needs and job market in AI
Marketable Skills
- Code using AI programming skills
- Design, collect, and analyze data
- Solve problems with creative solutions
- Quickly grasp new concepts
- Collaborate and communicate in teams
Artificial Intelligence Master's Highlights
The College of Engineering has state-of-the-art instructional facilities and laboratories
containing cutting-edge research equipment in top-ranked research labs that offer
exciting possibilities for study and discovery.
Alongside faculty members, you’ll prepare you for the most in-demand AI jobs by exploring
areas of study such as autonomous systems, biomedical engineering, machine learning
and natural language processing.
Core courses will dive into deep learning, machine learning, big data and data science,
and feature engineering.
Bridge courses provide you the necessary background in programming and include Software
Development for Artificial Intelligence and Fundamentals of Artificial Intelligence.
Teaching and research assistantships are available to help you pursue your graduate
degree by providing you with a monthly stipend and may qualify you for in-state tuition
rates.
This is the only standalone Master of Science in Artificial Intelligence in the state
of Texas and one of few nationwide.
What Can You Do With A Degree in Machine Learning Artificial Intelligence?
The most in-demand jobs are data scientists, software engineers, and machine learning engineers, but career opportunities in artificial intelligence can span a wide array of disciplines.
Artificial Intelligence Master's Courses You Could Take
Introduction to Big Data and Data Science (3 hrs)
Introduction to Big Data and Data Science including an overview of the field, technical
challenges, computational approaches, practical applications, structured and unstructured
data processing, empirical methods in computer science, data analytics and learning,
data visualization, privacy and ethics.
Systems Modeling and Simulation (3 hrs)
Aims to systematically introduce the concepts and analytical tools required to abstract
engineering problems from applications, and to simulate and analyze such problems.
Topics include dynamical systems modeling, stochastic models, queuing models, Markov
chains, model identification, Monte-Carlo simulation, model reduction, agent-based
modeling, large-scale networks, and applications to ecological, biological, and modern
infrastructure systems.
AI for Wearables and Healthcare (3 hrs)
Students use machine learning to extract clinically useful signals from wearable devices
including inertial sensors such as accelerometers and gyroscopes. Applications of
AI in healthcare as a whole are discussed, with a specific emphasis on wearable devices.
Scientific Data Visualization (3 hrs)
Introduction to visualization methods in data exploration. Topics include the use
of space, form and color to communicate information; visualization of multi-dimensional
data; data reduction methods such as principal component analysis and regression;
methods for special domains such as geographic data and large graphs; and designing
and implementing interactive interfaces.
Data Mining (3 hrs)
Introduction to data mining which includes main data mining tasks, e.g. classification,
clustering, association rules, and outlier detection, and some of the latest developments,
e.g. mining spatial data and web data.
Machine Learning (3 hrs)
The theory and process to create systems that learn directly from data to make predictions
and decisions. Topics include a wide variety of supervised learning methods, both
regression and classification, with an emphasis on those that perform well on large
feature sets. Ensemble methods are used to combine independent approaches efficiently.
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