Master of Artificial Intelligence
The Master of Artificial Intelligence program teaches the foundational concepts and practical skills in artificial intelligence and its subfields of machine learning, deep learning, computer vision, natural language processing, probabilistic reasoning, and data analytics. The program requires 30 credit hours of coursework in artificial intelligence, interdisciplinary applications of AI, and computer science.
Curriculum
| Minimum Credits Required | 30 |
| Maximum 400-Level Credit | 10 |
| Minimum CS/CSP Credit | 18 |
| Code | Title | Credit Hours |
|---|---|---|
| Artificial Intelligence Core Courses | (6) | |
| CS 581 | Advanced Artificial Intelligen | 3 |
| CS 584 | Machine Learning | 3 |
| or MATH 569 | Statistical Learning | |
| Artificial Intelligence Electives | (9-21) | |
| Select 9 to 21 credit hours from the following: | 9-21 | |
| Computer Vision | 3 | |
| Deep Learning | 3 | |
| Interact/Trans Mach Learning | 3 | |
| Online Social Network Analysis | 3 | |
| Probabilistic Graphical Models | 3 | |
| Natural Language Processing | 3 | |
| Data Processing and Analytics Electives | (3-15) | |
| Select 3 to 15 credit hours from the following: | 3-15 | |
| Data Integration Warehousing | 3 | |
| Advanced Data Mining | 3 | |
| Advanced Database Organization | 3 | |
| Parallel and Distributed Proc | 3 | |
| Data-Intensive Computing | 3 | |
| Big Data Technologies | 3 | |
| Data Preparation and Analysis | 3 | |
| Interdisciplinary Electives | (0-12) | |
| Select 0 to 12 credit hours from the following: | 0-12 | |
| Neurobiology | 3 | |
| Bioinformatics | 3 | |
| BME Applications of Statistics | 3 | |
| Neurobiology | 2 | |
| Comp Neurosci II: Vision | 3 | |
| Cognitive Neuroscience | 2 | |
| Neuroimaging | 3 | |
| Quantitative Neural Function | 3 | |
| Business Statistics | 3 | |
| UAV in Construction Projects | 3 | |
| Ststcl Qlty Process Control | 3 | |
| Intro to Linguistics | 3 | |
| Humanizing Technology | 3 | |
| AI in Smart Grid | 3 | |
| Machine Learning in Finance: | 3 | |
| Introduction to Time Series | 3 | |
| Applied Statistics | 3 | |
| Bayesian Computational Stats | 3 | |
| Predictive Analytics | 3 | |
| Introduction to Robotics | 3 | |
| Data Driven Modeling | 3 | |
| Robotics | 3 | |
| Statistical Analys in Fin Mkts | 3 | |
| Computational Finance | 3 | |
| Science and Values | 3 | |
| Ethics in Computer Science | 3 | |
| Learning Theory | 3 | |
| Cognitive Science | 3 | |
| Cognitive and Affective Bases | 3 | |
| CS Electives | (0-12) | |
| Select 0 to 12 credit hours of 400-level and above CS or CSP courses except CS 401 and 402 and 403 and 406 and 491 and 497 and 591 and 691 and 695. | 0-12 | |
