Artificial Intelligence
The MS in Artificial Intelligence program consists of 30 graduate-level semester credit hours, of which 12 are foundation, 9 are concentration, and 9 are elective (including the three options of coursework or project or thesis). A concentration must be declared by admitted students.
The program includes 4 concentrations, in (1) Computer Vision, (2) Machine Learning, (3) Knowledge Management and Reasoning, and (4) Intelligent Interaction. Students must choose one of three options: coursework, MS project, or MS thesis.
The program may be completed entirely on campus, entirely online, or through a combination of on-campus and online courses.
Requirements
To satisfy the requirements for the MS degree in Artificial Intelligence, all students admitted to the program are expected to complete a minimum of thirty semester hours of graduate coursework, with a cumulative grade point average of B or better. The program of study consists of core courses, concentration courses, and electives with coursework/project/thesis options.
Minimum Grade Requirement in addition to maintaining a minimum cumulative GPA of 3.0 or higher every semester:
- Courses in which grades of C- or below are earned cannot be used to fulfill degree requirements.
- A minimum of a 3.0 cumulative GPA or higher is required at the time of graduation.
| Code | Title | Credit Hours |
|---|---|---|
| Required Core (12 credits): | 12 | |
| Artificial Intelligence | ||
| Computational Learning | ||
or ECE 579 | Intelligent Systems | |
| Algorithm Analysis and Design | ||
| Software Engineering | ||
Concentrations
Students must choose one concentration (Computer Vision, Intelligent Interaction, Knowledge Management and Reasoning, Machine Learning) and complete 3 courses (9 credits) from the selected concentration.
Electives and Options
(9 credits): Any course(s) from an MS in AI concentration area(s) outside the student’s selected concentration can be an elective course(s). Additionally, the elective course(s) can be drawn from other CECS and partner college courses by faculty advisor or program director approval (excluding ENGR 500 and ENGR 501). The total number of elective courses should be three, including one of three options: (i) Coursework: taking three elective courses; (ii) Project: taking an MS Project by completing a 1-semester project (through the MS Project course in lieu of an elective) and two additional elective courses, or (iii) Thesis: taking an MS Thesis by completing a 2-semester thesis project (through the MS Thesis course in lieu of two electives) and one additional elective course. It is mandatory that the student select one of these three options.
Option 1: Coursework. This option requires three elective courses from an MS in AI concentration area(s) outside the student’s selected concentration. The minimum requirements for this option are as follows:
- Foundation courses – 12 credit hours
- Concentration courses – 9 credit hours
- Elective courses — 9 credit hours
Option 2: MS Project. This option requires a project report describing the results of an independent study project under the supervision of the advisor. The scope of the research topic for the project should be defined in such a way that a full-time student could complete the requirements for a master’s degree in 24 months or 6 semesters following the completion of course work by regularly scheduling graduate research credits. The minimum requirements for this option are as follows:
- Foundation courses – 12 credit hours
- Concentration courses – 9 credit hours
- Elective courses — 6 credit hours
- Master’s project – 3 credit hours
Option 3: MS Thesis. This option requires a research thesis prepared under the supervision of the advisor. The thesis describes a research investigation and its results. The scope of the research topic for the thesis should be defined in such a way that a full-time student could complete the requirements for a master’s degree in 24 months or 6 semesters following the completion of course work by regularly scheduling graduate research credits. The minimum requirements for this option are as follows:
- Foundation courses – 12 credit hours
- Concentration courses – 9 credit hours
- Elective courses – 3 credit hours
- Master’s Thesis — 6 credit hours
Concentrations
Select one of the following concentrations and complete 3 courses (9 credits) from the selected concentration:
| Code | Title | Credit Hours |
|---|---|---|
| Computer Vision Concentration | ||
| Select 3 courses (9 credits) from the following: | 9 | |
| Computer Graphics | ||
| Advanced Computer Graphics | ||
| Information Visualization and Virtualization | ||
| Advanced Information Visualization and Virtualization | ||
| Engineering in Virtual World | ||
| Pattern Recognition | ||
| Digital Image Processing | ||
| Sel Top:Image Proc/Mach Vision | ||
| Robot Vision | ||
| Pat Rec & Neural Netwks | ||
| Information Visualization | ||
| Code | Title | Credit Hours |
|---|---|---|
| Intelligent Interaction Concentration | ||
| Select 3 courses (9 credits) from the following: | 9 | |
| Wireless Sensor Networks and IoT | ||
| Trustworthy Artificial Intelligence | ||
| Advanced Artificial Intelligence | ||
| Computer Game Design and Implementation | ||
| Computer Game Design II | ||
| Edge Computing | ||
| Research Advances in Computational Game Theory and Economics | ||
| Intelligent Vehicle Systems | ||
| Intro Robot Syst | ||
| Mobile Robots | ||
| Res.Meth.Human Fctrs/Ergonomic | ||
| Human-Computer Interaction | ||
| Code | Title | Credit Hours |
|---|---|---|
| Knowledge Management and Reasoning Concentration | ||
| Select 3 courses (9 credits) from the following: | 9 | |
| Introduction to Natural Language Processing | ||
| Text Mining and Information Retrieval | ||
| Foundation of Information Security | ||
| Information Visualization and Virtualization | ||
| Decision Support and Expert Systems | ||
| Data Mining | ||
or ECE 537 | Data Mining | |
| Computational Learning | ||
| Trustworthy Artificial Intelligence | ||
| Deep Learning | ||
| Advanced Artificial Intelligence | ||
| Advanced Data Management | ||
| Research Advances in Artificial Intelligence | ||
| Advanced Data Mining | ||
| Analytic and Comp Math | ||
| Probability & Statistical Mod | ||
| Multivariate Statistics | ||
| Code | Title | Credit Hours |
|---|---|---|
| Machine Learning Concentration | ||
| Select 3 courses (9 credits) from the following: | 9 | |
| Introduction to Natural Language Processing | ||
| Introduction to Quantum Computing | ||
| Text Mining and Information Retrieval | ||
| Computational Learning | ||
| Deep Learning | ||
| Advanced Artificial Intelligence | ||
| Fuzzy Systems | ||
| Stochastic Processes | ||
| Intelligent Systems | ||
| Artificial Neural Networks | ||
| Adv Intelligent Sys | ||
| Optimization | ||
| Advanced Stochastic Processes | ||
Leaning Goals
- Understand representations, algorithms and techniques used across works in artificial intelligence and be able to apply and evaluate them in applications as well as develop their own.
- Understand and apply machine-learning techniques, in particular to draw inferences from data and help automate the development of AI systems and components.
- Understand the various ways and reasons humans are integrated into mixed human-AI environments, whether it is to improve overall integrated system performance, improve AI performance or influence human performance and learning.
- Understand the ethical concerns in developing responsible AI technologies.
- Implement AI systems, model human behavior, and evaluate their performance.