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.


Minimum Credits Required 30
Maximum 400-Level Credit 10
Minimum CS/CSP Credit 18
Artificial Intelligence Core Courses (6)
CS 581Advanced Artificial Intelligence3
CS 584Machine Learning3
or MATH 569 Statistical Learning
Artificial Intelligence Electives (9-21)
Select 9 to 21 credit hours from the following:9-21
Computer Vision3
Deep Learning3
Interactive and Transparent Machine Learning3
Online Social Network Analysis3
Probabilistic Graphical Models3
Natural Language Processing3
Data Processing and Analytics Electives (3-15)
Select 3 to 15 credit hours from the following:3-15
Data Integration, Warehousing, and Provenance3
Advanced Data Mining3
Advanced Database Organization3
Parallel and Distributed Processing3
Data-Intensive Computing3
Big Data Technologies3
Data Preparation and Analysis3
Interdisciplinary Electives (0-12)
Select 0 to 12 credit hours from the following:0-12
Biomedical Engineering Applications of Statistics3
Computational Neuroscience II: Vision3
Cognitive Neuroscience2
Quantitative Neural Function3
Business Statistics3
Applications of Unmanned Aerial Vehicles (UAVs or "Drones") for Construction Projects3
Statistical Quality and Process Control3
Introduction to Linguistics3
Humanizing Technology3
Artificial Intelligence in Smart Grid3
Machine Learning in Finance: From Theory to Practice 3
Introduction to Time Series3
Applied Statistics3
Bayesian Computational Statistics3
Predictive Analytics3
Introduction to Robotics3
Data Driven Modeling3
Statistical Analysis in Financial Markets3
MSF 526
Science and Values3
Ethics in Computer Science3
Learning Theory3
Cognitive Science3
Learning and Cognition3
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