Master of Science in Artificial Intelligence

For More Information

Campus address: Gates Dell Complex (GDC) 2.702, phone (512) 232-7407, fax (512) 471-8885; campus mail code: D9500

Mailing address: The University of Texas at Austin, Department of Computer Science, 2317 Speedway D9500, Austin, TX 78712

E-mail: msai@utexas.edu (prospective) / msaigradcoordinator@utexas.edu (graduate coordinator)

URL: https://cdso.utexas.edu/msai

Facilities for Graduate Work

This degree is 100% online and uses none of the physical facilities associated with The University of Texas at Austin, nor the department providing the degree. Students in the program will have access to university electronic resources such as library services.

Areas of Study

Graduate degree candidates are expected to develop broad competence in the discipline of Artificial Intelligence as a whole. The Master of Science in Artificial Intelligence is a 100% online program, with recommended completion models of one-and-a-half to three years. This program will provide working professionals with an opportunity to develop technical expertise in areas that contribute to the simulation of human learning and reasoning process and the modeling of human motor control and motion (e.g. deep learning, computer vision, information retrieval, robotics, human-AI interaction, natural language processing, etc.). The program will provide its graduates with the skill sets necessary to work across a host of sectors including, but not limited to, forecasting consumer behavior, fraud detection, energy, healthcare, intellectual property, manufacturing, and software development.

Admission Requirements

To be considered for admission, candidates must have a bachelor’s degree from a regionally accredited institution in the United States or a comparable degree from a foreign academic institution in a related field that demonstrates their capacity for success in a technical field of study. Common examples would include computer science, computer engineering, electrical engineering, statistics, data science, or mathematics. Applicants with degrees in non-technical subject areas may be eligible for admission if the admissions committee determines they have completed sufficient relevant coursework to be fully prepared to pursue graduate study. Courses to demonstrate preparedness can be found at this link. These courses should be of a technical nature and are typically offered by a CS, engineering, math, or statistics department and will not be counted toward the degree.

Graduate Studies Committee

The following faculty members served on the Graduate Studies Committee (GSC) in the spring 2023 semester.

Joydeep Biswas Constantine Caramanis Eunsol Choi Isil Dillig Gregory C Durrett Warren A Hunt Jr Adam R Klivans

Philipp Kraehenbuehl Qiang Liu Roberto Martin-Martin Raymond J Mooney Sujay Sanghavi Peter H Stone