UT San Antonio's College of AI, Cyber and Computing (CAICC) is driven by the goals of educating students in artificial intelligence (AI), computing, cybersecurity, and data science, and achieving global recognition as a world-class public research university that is future-oriented and urban-serving. CAICC positions the university as a leader in the rapidly evolving landscape of advanced technologies by strengthening the economic and workforce impact through the production of highly skilled and innovative graduates, not only for San Antonio but across Texas, the nation, and beyond.
CAICC is comprised of four academic departments, 7 master's degrees, 4 doctoral degrees, and 7 graduate certificates.
CAICC Programs
Department of Computer Engineering
Department of Computer Science
Department of Information Systems and Cybersecurity
Department of Statistics and Data Science
Degree-Specific Requirements
All program requirements should be unchanged from previous versions of the 2025-2027 Graduate Catalog. To confirm your degree requirements, you can visit DegreeWorks or consult your Graduate Advisor of Record.
Master of Science in Artificial Intelligence
The Master of Science degree in Artificial Intelligence program is designed to train and equip graduate students in core AI concepts that will fortify their career prospects in AI or related fields. The program comprises three concentrations—1) Analytics, 2) Computer Science, and 3) Intelligent and Autonomous Systems—which provide a broad spectrum of courses for graduate students to specialize in sub-areas within the AI field. Through these concentrations, the program trains graduate students in the design, development, use, and deployment of AI technologies. Curated AI courses provide students with a repertoire of AI skills and tools for effectively solving problems in a specific domain and extend the knowledge to advance their respective disciplines. The program also offers a multidisciplinary environment that supports industry-readiness in innovative AI sub-fields. A thesis option is offered for students who want the opportunity to obtain expertise in research and who may be interested in pursuing a doctoral degree in AI-related fields. A non-thesis option is available for students who prefer a practical applications-oriented degree.
Program Admission Requirements
In addition to the University-wide graduate admission requirements, admission decisions will be based on a combination of the following:
- A bachelor’s degree in engineering, sciences, mathematics, or in related fields for exceptional candidates.
- A Statement of Purpose.
- A current résumé.
- Two letters of recommendation.
- A minimum grade point average of 3.0 in the last 60 semester credit hours of coursework.
- A minimum score of 79 on the Test of English as a Foreign Language (TOEFL) iBT or 6.5 on the International English Language Testing System (IELTS), for students whose native language is not English.
Submission of the Graduate Record Examination (GRE) is optional. A student who does not qualify for unconditional admission may be admitted on a conditional basis as determined by the AI Core Committee.
Degree Requirements
The M.S. in AI program is offered with both Thesis and Non-Thesis options. A minimum of 30 semester credit hours are required to complete the program, including 9 credit hours of core courses, 15 credit hours of concentration-required courses, and 6 credit hours of elective courses for the Non-Thesis Option or 6 credit hours of thesis/capstone project. Thesis and Non-Thesis students can take courses outside of the suggested courses below with approval from the Graduate Advisor of Record (GAR). All approved courses that count towards the degree should be listed on the students Program of Study. All incoming students are required to enroll in the core courses to achieve a common understanding and knowledge of AI foundations. The enrollment for the graduate thesis must be in consultation with the supervising professor and receive approval from the Program Director.
Thesis Option
The degree requires 30 semester credit hours, including 24 technical course credits and 6 thesis credits identified as Master’s Thesis in the specific concentration. Students should take 9 semester credit hours of common core courses in the first two semesters. 15 semester credit hours of required courses must be taken within the concentration area to satisfy the depth requirement. No more than 3 semester credit hours of independent study should be included. Depending on the concentration choice, 3 to 6 semester credit hours may be taken from other concentration courses with the approval of the Core Committee. The distribution of required courses is shown below.
Course List | Code | Title | Credit Hours |
| Artificial Intelligence | |
| Advanced Topics in Signal Processing and Machine Learning (Topic: Intro to Machine Learning) | |
| Introduction to Statistical Inference | |
| Data-Driven Decision Making and Design | |
| Data Analytics Tools and Techniques | |
| Data Analytics Visualization and Communication | |
| Data Analytics Applications | |
| Data Foundations | |
| Deep Learning on Cloud Platforms | |
| Special Problems | |
| Statistical Methods in Research and Practice I | |
| SAS Programming and Data Management | |
| R Programming for Data Science | |
| Statistical Modeling | |
| Predictive Modeling | |
| | |
| |
| Computer Vision | |
| Topics in Computer Science (Topic: Autonomous Driving) | |
| Topics in Computer Science (Topic: Robotics) | |
| Topics in Computer Science (Topic: Adversarial AI) | |
| Topics in Computer Science (Topic: Parallel and Distributed Machine Learning) | |
| Topics in Data Science (Topic: Brain Inspired AI) | |
| Multi-Agent Systems | |
| Cognitive Neuroscience Inspired Machine Learning | |
| Trust, Confidence and Explainability in Artificial Intelligence | |
| Natural Language Processing | |
| Deep Learning | |
| Deep Reinforcement Learning | |
| Quantum Machine Learning | |
| |
| Computer Architecture | |
| Operating Systems | |
| Analysis of Algorithms | |
| Topics in Computer Science (Topic: Developing AI Tools for K-12) | |
| |
| Engineering Programming | |
| Linear Systems and Control | |
| Random Signals and Noise | |
| Special Topics in Control (Topic: Reinforcement Learning) | |
| Special Topics in Control (Topic: Optimal Control and Applications) | |
| Special Topics in Control (Topic: Optimization and Control of Cyber Physical Systems) | |
| Special Topics in Control (Topic: Computational Intelligence) | |
| Special Topics in Control (Topic: Network Multi-Agent System) | |
| Special Topics in Control (Topic: Advanced Robotics and AI) | |
| Advanced Topics in Signal Processing and Machine Learning (Topic: Brain Inspired AI) | |
| Advanced Topics in Signal Processing and Machine Learning (Topic: AI in Engineering) | |
| Advanced Topics in Signal Processing and Machine Learning (Topic: Natural Language Processing w/Deep Learning) | |
| Special Problems |
| Advanced Topics in Signal Processing and Machine Learning (Topic: Computational Intelligence in Data Analysis) | |
| Statistical Modeling |
| Advanced Topics in Signal Processing and Machine Learning (Topic: Statistical Inference) | |
| Advanced Topics in Signal Processing and Machine Learning (Topic: Bioinformatics) | |
| Advanced Topics in Signal Processing (Topic: Deep Learning) | |
| |
| Master's Thesis | |
| Master's Thesis | |
| Master's Thesis | |
| Total Credit Hours | 30 |
Non-Thesis Option
The degree requires 30 semester credit hours of technical course credits. Students should take 9 semester credit hours of common core courses in the first two semesters. 15 semester credit hours of required courses must be taken within the concentration area to satisfy the depth requirement. No more than 3 semester credit hours of independent study should be included. Depending on the concentration choice, 3 to 6 semester credit hours may be taken from other concentration courses with approval of the Core Committee. An additional 6 semester credit hours of elective courses must be taken from the concentration or outside the concentration. The distribution of required courses is given below.
Course List | Code | Title | Credit Hours |
| Artificial Intelligence | |
| Advanced Topics in Signal Processing and Machine Learning (Top: Intro to Machine Learning) | |
| Introduction to Statistical Inference | |
| Data Foundations | |
| Deep Learning on Cloud Platforms | |
| Special Problems | |
| SAS Programming and Data Management | |
| R Programming for Data Science | |
| Statistical Modeling | |
| Predictive Modeling | |
| Statistical Methods in Research and Practice I | |
| Data-Driven Decision Making and Design | |
| Data Analytics Tools and Techniques | |
| Data Analytics Visualization and Communication | |
| Data Analytics Applications | |
| | |
| |
| Computer Vision | |
| Topics in Data Science (Topic: Brain Inspired AI) | |
| Multi-Agent Systems | |
| Cognitive Neuroscience Inspired Machine Learning | |
| Trust, Confidence and Explainability in Artificial Intelligence | |
| Natural Language Processing | |
| Deep Learning | |
| Deep Reinforcement Learning | |
| Quantum Machine Learning | |
| Topics in Computer Science (Topic: Autonomous Driving) | |
| Topics in Computer Science (Topic: Robotics) | |
| Topics in Computer Science (Topic: Adversarial AI) | |
| Topics in Computer Science (Topic: Parallel and Distributed Machine Learning) | |
| |
| Computer Architecture | |
| Operating Systems | |
| Analysis of Algorithms | |
| |
| Engineering Programming | |
| Linear Systems and Control | |
| Random Signals and Noise | |
| Special Topics in Control (Topic: Reinforcement Learning ) | |
| Special Topics in Control (Topic: Optimal Control and Applications ) | |
| Special Topics in Control (Topic: Optimization & Control of Cyber Physical Systems) | |
| Special Topics in Control (Topic: Computational Intelligence) | |
| Special Topics in Control (Topic: Network Multi-Agent System) | |
| Special Topics in Control (Topic: Advanced Robotics and AI) | |
| Advanced Topics in Signal Processing and Machine Learning (Topic: Brain Inspired AI) | |
| Advanced Topics in Signal Processing and Machine Learning (Topic: AI in Engineering) | |
| Advanced Topics in Signal Processing and Machine Learning (Topic: Natural Language Processing w/Deep Learning ) | |
| Special Problems |
| Advanced Topics in Signal Processing and Machine Learning (Topic: Computational Intelligence in Data Analysis) | |
| Statistical Modeling |
| Advanced Topics in Signal Processing and Machine Learning (Topic: Statistical Inference) | |
| Advanced Topics in Signal Processing and Machine Learning (Topic: Bioinformatics) | |
| Advanced Topics in Signal Processing (Topic: Deep Learning) | |
| Total Credit Hours | 30 |
Dual Doctor of Medicine and Master of Science in Artificial Intelligence
The Doctor of Medicine (M.D.) and Master of Science (M.S.) in Artificial Intelligence (AI) Dual Degree is offered by UT Health San Antonio Long School of Medicine and UT San Antonio. This M.D./M.S. in AI is designed to prepare students for the next generation of healthcare advances by providing comprehensive training in applied artificial intelligence. Armed with this training, graduates can become future leaders in research, education, academia, industry, and healthcare administration, shaping the future of healthcare for all. Students will apply to the M.S. in AI degree and select one of three concentrations: 1) Analytics, 2) Computer Science, and 3) Intelligent and Autonomous Systems, which provide a broad spectrum of courses for graduate students to specialize in sub-areas within the AI field.
Program Admission Requirements
In addition to the University-wide graduate admission requirements, admission decisions will be based on a combination of the following:
-
Current enrollment in the Undergraduate Medical Education program at UT Health San Antonio
-
A minimum grade point average of 3.0 (on a 4.0 scale) in the last 60 semester credit hours of coursework.
-
For students whose native language is not English, a minimum score of 79 on the Test of English as a Foreign Language (TOEFL) iBT or 6.5 on the International English Language Testing System (IELTS) is required.
Submission of the Graduate Record Examination (GRE) is optional. A student who does not qualify for unconditional admission may be admitted on a conditional basis as determined by the AI Core Committee.
Degree Requirements
The M.D./M.S. in AI program is offered as a non-thesis degree program. A minimum of 30 semester credit hours are required to complete the program, including 9 credit hours of core courses, 15 credit hours of concentration required courses, and 6 credit hours of capstone project courses. All incoming students are required to enroll in the core courses to achieve a common understanding and knowledge of AI foundations. Additional courses offered at UT Health can be found in the School of Medicine Catalog.
Course List | Code | Title | Credit Hours |
| Artificial Intelligence | |
| Advanced Topics in Signal Processing and Machine Learning | |
| Introduction to Statistical Inference | |
| |
| Data-Driven Decision Making and Design | |
| Data Analytics Tools and Techniques | |
| Data Analytics Visualization and Communication | |
| Data Analytics Applications | |
| Data Foundations | |
| Deep Learning on Cloud Platforms | |
| Special Problems | |
| Statistical Methods in Research and Practice I | |
| SAS Programming and Data Management | |
| R Programming for Data Science | |
| Statistical Modeling | |
| Predictive Modeling | |
| |
| |
| |
| Computer Vision | |
| Topics in Computer Science (Topic: Autonomous Driving) | |
| Topics in Computer Science (Topic: Robotics) | |
| Topics in Computer Science (Topic: Adversarial AI) | |
| Topics in Computer Science (Topic: Parallel and Distributed Machine Learning) | |
| Topics in Data Science (Topic: Brain Inspired AI) | |
| Multi-Agent Systems | |
| Cognitive Neuroscience Inspired Machine Learning | |
| Trust, Confidence and Explainability in Artificial Intelligence | |
| Natural Language Processing | |
| Deep Learning | |
| Deep Reinforcement Learning | |
| Quantum Machine Learning | |
| |
| Computer Architecture | |
| Operating Systems | |
| Analysis of Algorithms | |
| |
| |
| Engineering Programming | |
| Linear Systems and Control | |
| Random Signals and Noise | |
| Special Topics in Control (Topic: Reinforcement Learning) | |
| Special Topics in Control (Topic: Optimal Control and Applications) | |
| Special Topics in Control (Topic: Optimization and Control of Cyber Physical Systems) | |
| Special Topics in Control (Topic: Computational Intelligence) | |
| Special Topics in Control (Topic: Network Multi-Agent System) | |
| Special Topics in Control (Topic: Advanced Robotics and AI) | |
| Advanced Topics in Signal Processing and Machine Learning (Topic: Brain Inspired AI) | |
| Advanced Topics in Signal Processing and Machine Learning (Topic: AI in Engineering) | |
| Advanced Topics in Signal Processing and Machine Learning (Topic: Natural Language Processing w/Deep Learning) | |
| Special Problems |
| Advanced Topics in Signal Processing and Machine Learning (Topic: Computational Intelligence in Data Analysis) | |
| Statistical Modeling |
| Advanced Topics in Signal Processing and Machine Learning (Topic: Statistical Inference) | |
| Advanced Topics in Signal Processing and Machine Learning (Topic: Bioinformatics) | |
| Advanced Topics in Signal Processing (Topic: Deep Learning) | |
| |
| Total Credit Hours | 30 |
