Considering a career in the exciting field of artificial intelligence? The College of Engineering’s new graduate program at the University of North Texas may be for you. It’s the only standalone Master of Science in Artificial Intelligence in the state of Texas and is one of few nationwide.

Graduate opportunities

UNT’s degree is interdisciplinary, allowing you to leverage your existing skill set and experience with AI knowledge. In our program, you’ll have the opportunity to specialize in machine learning, biomedical engineering or autonomous systems.

The most in-demand AI jobs are:

  • Data scientists
  • Machine learning engineers
  • Software engineers

But career opportunities in artificial intelligence can span a wide array of disciplines. Envision yourself in the health care industry? We offer a concentration in biomedical engineering. Are robotics, self-driving vehicles or drones more your speed? Then consider a path in autonomous systems. Prefer to learn more about the driving force behind advances in AI? Machine learning may be the key to your future success. Whichever route you choose, the marketable skills you’ll develop in this program are sure to prepare you for a challenging, rewarding career in AI.

Research opportunities

The College of Engineering has state-of-the-art instructional facilities and laboratories containing cutting-edge research equipment. Top-ranked research labs offer exciting possibilities for study and discovery. Alongside faculty members, you’ll explore areas of study that will prepare you for the most in-demand AI jobs. These areas are:

  • Autonomous systems
  • Biomedical engineering
  • Machine learning
  • Natural language processing

Attending UNT

Admission requirements

Our program is open to students from engineering, computer science, math and other science-related backgrounds. Your transcript will be reviewed to determine if your background is suitable for admission into the concentration of your choice and/or if leveling coursework is required.

Degree requirements

You can expect to take:

  • 6 hours of bridge courses
  • 15 hours of core courses
  • 12 hours of courses in your chosen concentration

Bridge courses provide you the necessary background in programming and include Software Development for Artificial Intelligence and Fundamentals of Artificial Intelligence.

Core courses will dive into:

  • Big data
  • Big science and feature engineering
  • Deep learning
  • Machine learning

Choose your own career path with concentration focus areas in:

  • Autonomous systems
  • Biomedical engineering
  • Machine learning

Financial assistance

Teaching and research assistantships are available to help you pursue your graduate degree. These assistantships provide you with a monthly stipend and may qualify you for in-state tuition rates. You also can apply for UNT scholarships as well as departmental scholarships within the College of Engineering. Apply and learn more. The deadline is March 1.

Faculty

Mark Albert, Assistant Professor; Ph.D., Cornell University. Machine learning, biomedical engineering.

Eduardo Blanco, Associate Professor; Ph.D., The University of Texas at Dallas. Natural language processing, computational semantics.

Bill Buckles, Professor; Ph.D., University of Alabama in Huntsville. Image understanding and related problems in search optimization and pattern recognition.

Parthasarathy Guturu, Associate Professor; Ph.D., Indian Institute of Technology. Intelligent algorithms for ad-hoc sensor networks, ad-hoc and fixed infrastructure network composites, multi-sensor data fusion, medical imaging and bioinformatics.

Yan Huang, Professor; Ph.D., University of Minnesota. Spatio-temporal databases and mining, geo-stream data processing, smart transportation and location-based social networks.

Wei Jin, Associate Professor; Ph.D., University at Buffalo, State University of New York. Text and web data mining, information retrieval, biomedical and health care informatics.

Brian Meckes, Assistant Professor; Ph.D., University of California, San Diego. Nanotechnology, therapeutic design and cellular engineering.

Rodney Nielsen, Associate Professor; Ph.D., University of Colorado at Boulder. Natural language processing, machine learning and cognitive science.

Paul Tarau, Professor, Ph.D., Université de Montreal. Natural language processing, logic programming, deep learning, type inference and theorem proving, compilers and abstract machines, tree-based arithmetic systems and combinatorics of lambda terms.

Xiaohui Yuan, Associate Professor; Ph.D., Tulane University. Computer vision, artificial intelligence and machine learning.