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Artificial Intelligence

The multidisciplinary studies degree in Artificial Intelligence allows students to study multiple fields such as computer science, mathematics, statistics, electrical and computer engineering, and information systems. Artificial Intelligence is the simulation of human intelligence processes by computer systems which includes machine learning, reasoning and self-correction. Companies like Apple, Amazon, Tesla, Netflix, Google and others use artificial intelligence for speech recognition, voice powered personal assistants, self-driving vehicles, and robotics.


BS MDST - Artificial Intelligence 20-22 catalog (BS-MDAI-UC)

FALL SPRING
FIRST YEAR
AIS 1203 or 1243 Academic Inquiry and Scholarship 3 CS 1714 Computer Programming II (FA 2) 4
CS 1063 Intro to Computer Programming I 3 MAT 1224  Calculus II (FA 1) 4
MAT 1214 Calculus I (FA 1) 4 STA 3003 Applied Statistics (FA 1) 3
CORE Creative Arts 3 WRC 1023 Freshman Comp II (Q) 3
WRC 1013 Freshman Comp I (Q) 3    
TOTAL   16   TOTAL   14
   
SECOND YEAR
CS 2124 Data Structures/Rec. (FA 2) 4   CS 2233 Discrete Math (FA 1) 3
MAT 2233 or EGR 2323 Linear Algebra (FA 1)/Engineering Analysis (FA 1) 3   CS 3424 Systems Prog/Rec (FA 2) 4
STA 3513 Probability and Statistics (FA 1) 3   CORE Component Area Option 3
MDS 2023 Intro to Multidisc Studies 3   POL 1013 Introduction to American Politics 3
POL 1133 or 1213 Texas Politics and Society 3    
TOTAL   16   TOTAL   13
   
THIRD YEAR
CS 3343 Analysis of Algorithms (FA2) 3   ELEC Programming, Data Structure... (FA 2) 3
STA 3523  Mathematical Statistics (FA 1) 3   ELEC Programming, Data Structure... (FA 2) 3
ELEC AI & Application (FA 3) 3   ELEC AI & Application (FA 3) 3
COM   Communication Req 3   ELEC AI & Application (FA 3) 3
CORE Life & Physical Sciences 3   CORE Life & Physical Sciences 3
TOTAL   15   TOTAL   15
   
FOURTH YEAR
ELEC Programming, Data Structure... (FA 2) 3/1   ELEC AI & Application (FA 3) 3
ELEC AI & Application (FA 3) 3   ELEC AI & Application (FA 3) 3
ELEC AI & Application (FA 3) 3   MDS 4983 Seminar for Multidisc Studies 3
CORE Social & Behavioral Sciences 3   CORE Lang, Philosophy, & Cult. 3
CORE American History 3   CORE American History 3
TOTAL   16   TOTAL   15

Focus Area 1 - Mathematics and Statistics - 20 hours (14 hours prescribed)

Course Number  Title  Prerequisite 
MAT 1214 Calculus I (required) MAT 1093
MAT 1224 Calculus II (required) MAT 1214
MAT 2233 or Linear Algebra or EGR 2233 (required) MAT 1224 
EGR 2323 Engineering Analysis or MAT 2323  MAT 1224 
CS 2233 Discrete Mathematical Structures (required) CS 1714 and MAT 1214
STA 3003 Applied Statistics (required) MAT 1214
STA 3513 Probability and Statistics STA 3003 and MAT 1224
STA 3523 Mathematical Statistics STA 3513
CE 3173 Numerical Methods CS 1173 and EGR 2323
EE 3423 Mathematics in Signals and Systems EE 2423 and EGR 2323
EE 3533 Probability and Stochastic Processes EE 3423 and EGR 2323
MR 2173 Numerical Methods EGR 2323

Focus Area 2 - Programming, Data Structures & Algorithms - 25 hours (15 hours prescribed)

Course Number  Title  Prerequisite (C- or better)
CS 1714 Computer Programming II (required) CS 1063
CS 2124 Data Structures (required) CS 1714 and MAT 1214
CS 3343 Analysis of Algorithms (required) CS 2124 and CS 2233 and CS 3333
CS 3424 Systems Programming (required) CS 2124
CS 3443 Application Programming CS 2124
CS 3743 Database Systems CS 2233 and CS 3424
CS 3844 Computer Organization CS 2124
     
EE 3463 Microcomputer Systems I EE 2513 and CS 2073
EE 3223 C++ and Data Structures EE 3463
EE 3233 Systems Programming for Engineers EE 3223
EE 3563 Digital System Design EE 2511 and EE 2513
EE 4243 Computer Organization and Architecture EE 3463
     
IS 2053 (IS 2043) Programming Languages I with Scripting  IS 1003

Focus Area 3 - Artificial Intelligence and Applications - 21 hours

Course Number  Title  Prerequisite (C- or better)
  AI and Data Science  
STA 3333 Introduction to Data Science and Analysis STA 1053
STA 4133 or  Intro to Prog & Data Manage SAS    
STA 4233 Intro to Prog & Data Manage R    
STA 4643 Introduction to Stochastic Processes STA 3513
STA 4713 Applied Regression Analysis STA 3003
STA 4723 Introduction to the Design of Experiments STA 3003
STA 4753 Time-Series Analysis STA 3513
     
CS 3443 Application Programming CS 2124
CS 3743 Database Systems CS 2124 and CS 3424
CS 3753 Data Science CS 2124, CS 2233 and CS 3333 (need instructor consent)
CS 3793 Artifical Intelligence CS 3343 and CS 3424
CS 4243 Large Scale Data Management CS 3423 and CS 3443
CS 4373 Data Mining CS 3343
CS 4413 Web Technologies CS 3424
CS 4593 Topics in Computer Science Consent of Instructor
CS 4843 Cloud Computing CS 3424
CS 4973 Advanced Topics in Systems and Clouds Consent of Instructor
IS 3523 Intrusion Detection IS 3513
IS 4023 Applied Big Data with Machine Learning IS 3073
IS 4463 Web Application Security IS 3513
IS 4483 Digital Forensic Analysis I   
IS 4513 Industrial Control Systems IS 3513
IS 4523 Digital Forensic Analysis II  IS 4483
EE 3113 Electrical and Computer Engineering Laboratory I EE 2423, EE 2513 and EE 3313
EE 3223 C++ and Data Structures EE 3463
EE 4243 Computer Organization and Architecture EE 3463
EE 4443 Discrete- Time and Computer Controlled Systems EE 3413
EE 4673 Data Communication and Networks EE 3223 and EE 4613
  Neuroscience and Health  
BIO 4583 The Computational Brain BIO 2313 & BIO 3433
CS 4223 Bioinformatics and Big Data CS 3343 or consent of instructor
CS 4233 Computational Biology and Bioinformatics CS 3343
  Advanced Mathematics  
MAT 4113 Computer Mathematical Topics MAT 1214
  Robotics  
EE 3413  Analysis and Design of Control Systems EE 3423 & EGR 2213
EE 4723  Intelligent Robotics EE 3413
EE 4953 Special Studies in Electrical and Computer Engineering Consent of Instructor
ME 4773  Robotics EGR 2513 and ME 2173