Program Overview

Artificial Intelligence Master's

Master of Science (M.S.)

Program Type

• Major

Format

• On Campus
• Online

Estimated Time to Complete

• 2-3 years

Credit Hours

• 33

Why 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.

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.

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

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. (3 hrs)

Systems Modeling and Simulation

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. (3 hrs)

AI for Wearables and Healthcare

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. (3 hrs)

Scientific Data Visualization

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. (3 hrs)

Data Mining

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. (3 hrs)

Machine Learning

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. (3 hrs)