Master of Artificial Intelligence
Artificial Intelligence now stands at the cutting edge of our fast-paced digital society. It impacts nearly every aspect of our lives and society, from healthcare to transportation; from finance to education; and from manufacturing to entertainment. The Master of Artificial Intelligence program offers a deep and broad exploration of this transformative field. You will learn 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 Intelligence | 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 | |
| Interactive and Transparent Machine 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, and Provenance | 3 | |
| Advanced Data Mining | 3 | |
| Advanced Database Organization | 3 | |
| Parallel and Distributed Processing | 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 | |
| Biomedical Engineering Applications of Statistics | 3 | |
| Neurobiology | 2 | |
| Computational Neuroscience II: Vision | 3 | |
| Cognitive Neuroscience | 2 | |
| Neuroimaging | 3 | |
| Quantitative Neural Function | 3 | |
| Business Statistics | 3 | |
| Applications of Unmanned Aerial Vehicles (UAVs or "Drones") for Construction Projects | 3 | |
| Statistical Quality and Process Control | 3 | |
| Introduction to Linguistics | 3 | |
| Humanizing Technology | 3 | |
| Artificial Intelligence in Smart Grid | 3 | |
| Machine Learning in Finance: From Theory to Practice | 3 | |
| Introduction to Time Series | 3 | |
| Regression | 3 | |
| Bayesian Computational Statistics | 3 | |
| Predictive Analytics | 3 | |
| Introduction to Robotics | 3 | |
| Data Driven Modeling | 3 | |
| Robotics | 3 | |
| Statistical Analysis in Financial Markets | 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 | |
