College of AI, Cyber and Computing | The University of Texas at San Antonio Undergraduate Catalog Academic Catalog

College of AI, Cyber and Computing | The University of Texas at San Antonio Undergraduate Catalog Academic Catalog
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College of AI, Cyber and Computing
College of AI, Cyber and Computing
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
M.S. in Artificial Intelligence
Dual M.S./M.N.A. in Artificial Intelligence
Dual M.D./M.S. in Artificial Intelligence
Department of Computer Engineering
M.S. in Computer Engineering
Integrated Bachelor's/Master's Program
Ph.D. in Computer Engineering
Department of Computer Science
M.S. in Computer Science
M.S. in Cybersecurity Science
Ph.D. in Computer Science
Graduate Certificate in Cloud Computing
Department of Information Systems and Cybersecurity
M.S. in Information Technology
Cyber Security Concentration
Cyber Analytics Concentration
Dual M.S.I.T. with a Cyber Security Concentration / Master in Cybersecurity
Ph.D. in Information Technology
Graduate Certificate in Cloud Computing
Graduate Certificate in Cybersecurity
Graduate Certificate in Intelligence Studies
Department of Statistics and Data Science
M.S. in Data Analytics
Accelerated Master of Science in Data Analytics
M.S. in Statistics and Data Science
Accelerated Master of Science in Statistics and Data Science
Ph.D. in Applied Statistics
Graduate Certificate in Data Engineering
Graduate Certificate in Data Science
Graduate Certificate in Predictive Analytics and Modeling
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.
M.S. in Artificial Intelligence
Dual M.S./M.N.A. in Artificial Intelligence
Dual M.D./M.S. in Artificial Intelligence
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
A. Required Core Courses
CS 5233
Artificial Intelligence
EE 5263
Advanced Topics in Signal Processing and Machine Learning (Topic: Intro to Machine Learning)
STA 5093
Introduction to Statistical Inference
B. Prescribed Electives (a set of five courses in the chosen concentration)
15
Analytics Concentration
DA 6213
Data-Driven Decision Making and Design
DA 6223
Data Analytics Tools and Techniques
DA 6233
Data Analytics Visualization and Communication
DA 6813
Data Analytics Applications
IS 6713
Data Foundations
IS 6733
Deep Learning on Cloud Platforms
IS 6973
Special Problems
STA 6003
Statistical Methods in Research and Practice I
STA 6033
SAS Programming and Data Management
STA 6233
R Programming for Data Science
STA 6443
Statistical Modeling
STA 6543
Predictive Modeling
CS/EE
Elective
Computer Science Concentration
Section 1: Select three to five courses from the following:
CS 5243
Computer Vision
CS 5463
Topics in Computer Science (Topic: Autonomous Driving)
CS 5463
Topics in Computer Science (Topic: Robotics)
CS 5463
Topics in Computer Science (Topic: Adversarial AI)
CS 5463
Topics in Computer Science (Topic: Parallel and Distributed Machine Learning)
CS 5483
Topics in Data Science (Topic: Brain Inspired AI)
CS 5593
Multi-Agent Systems
CS 5813
Cognitive Neuroscience Inspired Machine Learning
CS 5823
Trust, Confidence and Explainability in Artificial Intelligence
CS 6263
Natural Language Processing
CS 6283
Deep Learning
CS 6313
Deep Reinforcement Learning
CS 6383
Quantum Machine Learning
Section 2: Select up to two courses from the following:
CS 5513
Computer Architecture
CS 5523
Operating Systems
CS 5633
Analysis of Algorithms
CS 5463
Topics in Computer Science (Topic: Developing AI Tools for K-12)
Intelligent and Autonomous Systems Concentration
Additional electives may be chosen from other AI concentrations.
EE 5103
Engineering Programming
EE 5143
Linear Systems and Control
EE 5153
Random Signals and Noise
EE 5243
Special Topics in Control (Topic: Reinforcement Learning)
EE 5243
Special Topics in Control (Topic: Optimal Control and Applications)
EE 5243
Special Topics in Control (Topic: Optimization and Control of Cyber Physical Systems)
EE 5243
Special Topics in Control (Topic: Computational Intelligence)
EE 5243
Special Topics in Control (Topic: Network Multi-Agent System)
EE 5243
Special Topics in Control (Topic: Advanced Robotics and AI)
EE 5263
Advanced Topics in Signal Processing and Machine Learning (Topic: Brain Inspired AI)
EE 5263
Advanced Topics in Signal Processing and Machine Learning (Topic: AI in Engineering)
EE 5263
Advanced Topics in Signal Processing and Machine Learning (Topic: Natural Language Processing w/Deep Learning)
or
IS 6973
Special Problems
EE 5263
Advanced Topics in Signal Processing and Machine Learning (Topic: Computational Intelligence in Data Analysis)
or
STA 6443
Statistical Modeling
EE 5263
Advanced Topics in Signal Processing and Machine Learning (Topic: Statistical Inference)
EE 5263
Advanced Topics in Signal Processing and Machine Learning (Topic: Bioinformatics)
EE 6363
Advanced Topics in Signal Processing (Topic: Deep Learning)
C. Thesis
A minimum of 6 semester credit hours are needed. Students must enroll in the Thesis section belonging to their concentration.
CS 6983
Master's Thesis
EE 6983
Master's Thesis
STA 6983
Master's Thesis
Total Credit Hours
30
Additional
elective courses
can be
approved by the Graduate Studies Committee
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
A. Required Core Courses
CS 5233
Artificial Intelligence
EE 5263
Advanced Topics in Signal Processing and Machine Learning (Top: Intro to Machine Learning)
STA 5093
Introduction to Statistical Inference
B. Prescribed Electives (a set of five courses in the chosen concentration)
15
Analytics Concentration
IS 6713
Data Foundations
IS 6733
Deep Learning on Cloud Platforms
IS 6973
Special Problems
STA 6033
SAS Programming and Data Management
STA 6233
R Programming for Data Science
STA 6443
Statistical Modeling
STA 6543
Predictive Modeling
STA 6003
Statistical Methods in Research and Practice I
DA 6213
Data-Driven Decision Making and Design
DA 6223
Data Analytics Tools and Techniques
DA 6233
Data Analytics Visualization and Communication
DA 6813
Data Analytics Applications
CS/EE
Elective
Computer Science Concentration
Section 1: Select three to five courses from the following:
CS 5243
Computer Vision
CS 5483
Topics in Data Science (Topic: Brain Inspired AI)
CS 5593
Multi-Agent Systems
CS 5813
Cognitive Neuroscience Inspired Machine Learning
CS 5823
Trust, Confidence and Explainability in Artificial Intelligence
CS 6263
Natural Language Processing
CS 6283
Deep Learning
CS 6313
Deep Reinforcement Learning
CS 6383
Quantum Machine Learning
CS 5463
Topics in Computer Science (Topic: Autonomous Driving)
CS 5463
Topics in Computer Science (Topic: Robotics)
CS 5463
Topics in Computer Science (Topic: Adversarial AI)
CS 5463
Topics in Computer Science (Topic: Parallel and Distributed Machine Learning)
Section 2: Select up to two courses from the following:
CS 5513
Computer Architecture
CS 5523
Operating Systems
CS 5633
Analysis of Algorithms
Intelligent and Autonomous Systems Concentration
Additional electives may be chosen from other concentrations.
EE 5103
Engineering Programming
EE 5143
Linear Systems and Control
EE 5153
Random Signals and Noise
EE 5243
Special Topics in Control (Topic: Reinforcement Learning )
EE 5243
Special Topics in Control (Topic: Optimal Control and Applications )
EE 5243
Special Topics in Control (Topic: Optimization & Control of Cyber Physical Systems)
EE 5243
Special Topics in Control (Topic: Computational Intelligence)
EE 5243
Special Topics in Control (Topic: Network Multi-Agent System)
EE 5243
Special Topics in Control (Topic: Advanced Robotics and AI)
EE 5263
Advanced Topics in Signal Processing and Machine Learning (Topic: Brain Inspired AI)
EE 5263
Advanced Topics in Signal Processing and Machine Learning (Topic: AI in Engineering)
EE 5263
Advanced Topics in Signal Processing and Machine Learning (Topic: Natural Language Processing w/Deep Learning )
or
IS 6973
Special Problems
EE 5263
Advanced Topics in Signal Processing and Machine Learning (Topic: Computational Intelligence in Data Analysis)
or
STA 6443
Statistical Modeling
EE 5263
Advanced Topics in Signal Processing and Machine Learning (Topic: Statistical Inference)
EE 5263
Advanced Topics in Signal Processing and Machine Learning (Topic: Bioinformatics)
EE 6363
Advanced Topics in Signal Processing (Topic: Deep Learning)
C. Non-Thesis: 6 hours of electives from inside or outside concentration with advisor approval.
Total Credit Hours
30
Additional
elective courses
can be
approved by the Graduate Studies Committee
Dual Master of Science in Artificial Intelligence
This dual degree program is offered through the UT San Antonio College of AI, Cyber and Computing (CAICC), M.S. Artificial Intelligence concentration program, and the M.N.A. in Applied Artificial Intelligence at the Instituto Tecnológico de Monterrey (ITESM).
Applicants will be admitted to the M.S. Artificial Intelligence program and to the M.N.A. in Applied Artificial Intelligence ITESM in accordance with the admission schedules and policies of each institution. Applicants must submit all admission materials to each admission office independently and by the institution's deadline. Admission to the dual degree program may occur after a student has already matriculated in the M.S. Artificial Intelligence, as approved by each institution's program director.
Upon completion, CAICC will award the M.S. in Artificial Intelligence to each student who successfully completes the program, and ITESM will award the M.N.A. in Applied Artificial Intelligence.
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
For UT San Antonio students:
Program participants from UT San Antonio will enroll in UT San Antonio's M.S. Artificial Intelligence program and successfully complete the program's core requirements.
Students in the dual program who begin their education at UT San Antonio will take six (6) of 11 courses to fulfill the UT San Antonio M.S. Artificial Intelligence program requirement and the remaining five (5) courses at ITESM.
To earn the ITESM M.N.A. degree, UT San Antonio students must also complete a research essay jointly supervised by faculty members from both UT San Antonio and ITESM.
Additional information about the ITESM component for this dual program is available at
For ITESM students:
Program participants from ITESM will enroll in the M.N.A. in Applied Artificial Intelligence program in their first semester and successfully complete their core requirements.
Students in the Dual Program will take 18 credit hours (six courses) at UT San Antonio, as well as complete a research essay jointly supervised by faculty members from both UT San Antonio and ITESM.
The requirements listed here may change as determined by UT San Antonio and ITESM. Students are required to contact their respective institution's program director to review and confirm eligibility and detailed degree requirements.
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
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 i
the
School of Medicine Catalog
Course List
Code
Title
Credit Hours
A. Required Core Courses
CS 5233
Artificial Intelligence
EE 5263
Advanced Topics in Signal Processing and Machine Learning
STA 5093
Introduction to Statistical Inference
B. Prescribed Electives (a set of five courses in the chosen concentration)
15
Analytics Concentration
DA 6213
Data-Driven Decision Making and Design
DA 6223
Data Analytics Tools and Techniques
DA 6233
Data Analytics Visualization and Communication
DA 6813
Data Analytics Applications
IS 6713
Data Foundations
IS 6733
Deep Learning on Cloud Platforms
IS 6973
Special Problems
STA 6003
Statistical Methods in Research and Practice I
STA 6033
SAS Programming and Data Management
STA 6233
R Programming for Data Science
STA 6443
Statistical Modeling
STA 6543
Predictive Modeling
CS/EE Elective
Computer Science Concentration
Section 1: Select three to five courses from the following:
CS 5243
Computer Vision
CS 5463
Topics in Computer Science (Topic: Autonomous Driving)
CS 5463
Topics in Computer Science (Topic: Robotics)
CS 5463
Topics in Computer Science (Topic: Adversarial AI)
CS 5463
Topics in Computer Science (Topic: Parallel and Distributed Machine Learning)
CS 5483
Topics in Data Science (Topic: Brain Inspired AI)
CS 5593
Multi-Agent Systems
CS 5813
Cognitive Neuroscience Inspired Machine Learning
CS 5823
Trust, Confidence and Explainability in Artificial Intelligence
CS 6263
Natural Language Processing
CS 6283
Deep Learning
CS 6313
Deep Reinforcement Learning
CS 6383
Quantum Machine Learning
Section 2: Select up to two courses from the following:
CS 5513
Computer Architecture
CS 5523
Operating Systems
CS 5633
Analysis of Algorithms
Intelligent and Autonomous Systems Concentration
Additional electives may be chosen from other AI concentrations.
EE 5103
Engineering Programming
EE 5143
Linear Systems and Control
EE 5153
Random Signals and Noise
EE 5243
Special Topics in Control (Topic: Reinforcement Learning)
EE 5243
Special Topics in Control (Topic: Optimal Control and Applications)
EE 5243
Special Topics in Control (Topic: Optimization and Control of Cyber Physical Systems)
EE 5243
Special Topics in Control (Topic: Computational Intelligence)
EE 5243
Special Topics in Control (Topic: Network Multi-Agent System)
EE 5243
Special Topics in Control (Topic: Advanced Robotics and AI)
EE 5263
Advanced Topics in Signal Processing and Machine Learning (Topic: Brain Inspired AI)
EE 5263
Advanced Topics in Signal Processing and Machine Learning (Topic: AI in Engineering)
EE 5263
Advanced Topics in Signal Processing and Machine Learning (Topic: Natural Language Processing w/Deep Learning)
or
IS 6973
Special Problems
EE 5263
Advanced Topics in Signal Processing and Machine Learning (Topic: Computational Intelligence in Data Analysis)
or
STA 6443
Statistical Modeling
EE 5263
Advanced Topics in Signal Processing and Machine Learning (Topic: Statistical Inference)
EE 5263
Advanced Topics in Signal Processing and Machine Learning (Topic: Bioinformatics)
EE 6363
Advanced Topics in Signal Processing (Topic: Deep Learning)
C. Capstone
Students who earn a GPA of 3.0 or higher in both Capstone courses (INTD 4011 Machine Learning with AI I and INTD 4012 Machine Learning with AI II) will satisfy the comprehensive examination requirement for the M.D./M.S. in AI dual degree. Students must enroll in the Capstone Courses at UT Health.
Total Credit Hours
30
Additional
elective courses may be approved by the Graduate Studies Committee
The University of Texas at San Antonio
Office of the Registrar
One UTSA Circle
San Antonio, TX 78249
Phone:
210-458-8000
Toll Free Phone:
800-669-0919
Email:
onestop@utsa.edu
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