The MS in Artificial Intelligence degree provides students with the foundational knowledge and technical skills to design and implement artificial intelligence machines and autonomous systems. Students will gain a deep understanding of advanced concepts and techniques in AI, and will learn core relevant areas including machine learning, vision computing, natural language processing, robotics and cognitive science. The degree prepares students for professional careers as AI/ML engineers or research scientists, managing intelligent systems development and design.
| Program Requirements | Quarter Hours |
|---|---|
| Introductory Courses | 0-24 |
| Degree Requirements | 48 |
| Total hours required | 48-72 |
Learning Outcomes
Students will be able to:
- Analyze and design architectures for intelligent agents to solve a specific real-world problem.
- Explain core concepts of advanced machine learning techniques and AI algorithms including, for instance, heuristic search, logical reasoning, supervised and unsupervised learning, deep learning, classification methods, Bayesian networks, natural language processing and image analysis.
- Identify and combine AI techniques and approaches to solve a specific problem and to develop a modern intelligent application.
- Use programming skills to implement an AI enabled system in a specific domain.
- Explain and define categories of ethical dilemmas posed by the coming revolution in intelligent and autonomous computing systems.
- Work independently or in a small team to design and develop an intelligent computer system.
- Read and present research papers.
- Communicate effectively the core concepts of an intelligent computer system to a non-technical audience.
Degree Requirements
Course Requirements
No Introductory Course may be substituted for any other course at any level.
Introductory Courses
Introductory courses may be waived for any of the following conditions:
- The student has the appropriate course work to satisfy an Introductory Course.
- The student has appropriate and verified professional experience to satisfy an Introductory Course.
- If an exam is available, the student passes a Graduate Assessment Examination (GAE) in the Introductory Course area.
| Course | Title | Quarter Hours |
|---|---|---|
| CSC 400 | DISCRETE STRUCTURES FOR COMPUTER SCIENCE | 4 |
| CSC 401 | INTRODUCTION TO PROGRAMMING | 4 |
| CSC 402 | DATA STRUCTURES I | 4 |
| CSC 403 | DATA STRUCTURES II | 4 |
| CSC 412 | TOOLS AND TECHNIQUES FOR COMPUTATIONAL ANALYSIS | 4 |
| IT 403 | STATISTICS AND DATA ANALYSIS | 4 |
Required Courses
| Course | Title | Quarter Hours |
|---|---|---|
| CSC 421 | APPLIED ALGORITHMS AND STRUCTURES | 4 |
| CSC 480 | ARTIFICIAL INTELLIGENCE I | 4 |
| CSC 484 | ETHICS IN ARTIFICIAL INTELLIGENCE | 4 |
| CSC 578 | NEURAL NETWORKS AND DEEP LEARNING | 4 |
| CSC 580 | ARTIFICIAL INTELLIGENCE II | 4 |
| CSC 583 | NATURAL LANGUAGE PROCESSING | 4 |
| CSC 587 | COGNITIVE SCIENCE | 4 |
| DSC 540 | ADVANCED MACHINE LEARNING | 4 |
Major Electives
| Course | Title | Quarter Hours |
|---|---|---|
| Choose eight (8) credits from the following list: | 8 | |
| SPECIAL TOPICS IN ORGANIZATIONAL COMMUNICATION | ||
| SYMBOLIC PROGRAMMING | ||
| INTRODUCTION TO IMAGE PROCESSING | ||
| APPLIED IMAGE ANALYSIS | ||
| COMPUTER VISION | ||
| MINING BIG DATA | ||
| INTELLIGENT INFORMATION RETRIEVAL | ||
| RECOMMENDER SYSTEMS | ||
| TOPICS IN ARTIFICIAL INTELLIGENCE | ||
| INFORMATION SECURITY MANAGEMENT (FORMERLY CNS 440) | ||
| INTRODUCTION TO ROBOTICS (FORMERLY CSC 475) | ||
| DATABASE PROCESSING FOR LARGE-SCALE ANALYTICS | ||
| PROGRAMMING MACHINE LEARNING APPLICATIONS | ||
| SOCIAL NETWORK ANALYSIS | ||
| AI-DRIVEN SOFTWARE DEVELOPMENT | ||
| MACHINE LEARNING ENGINEERING FOR PRODUCTION (MLOPS) | ||
Open Elective
| Course | Title | Quarter Hours |
|---|---|---|
| Student must complete four (4) credits of advisor-approved graduate courses from the School of Computing in the range of 421-699. Students may select from the following subjects: CSC, CSE, CSEC, DSC, ECT, GAM, HIT, IS, IT, NET, SE. | 4 | |
Capstone Requirement
| Course | Title | Quarter Hours |
|---|---|---|
| Choose one course from the following list: | 4 | |
| CAPSTONE IN ARTIFICIAL INTELLIGENCE | ||
| MASTER'S RESEARCH | ||
| GRADUATE INTERNSHIP | ||
The internship option offers students the opportunity to integrate their academic experience with on-the-job training on an AI-related project. Students must enroll in CSC 697 for 4 credit hours to satisfy the capstone requirement. These are the steps: 1) Secure an internship with focus in AI. 2) International Students must obtain the appropriate practical training form and meet with an advisor in the CDM Academic Center for approval. ISS Forms 3) Login to MyCDM and click the “Internships” link on the left to start the course enrollment process.
A student who is working on a research project and has made an original contribution to their area of study may choose to complete a Master's Thesis. Additional information and requirements for School of Computing students pursuing the thesis option can be found on the SoC Master's Thesis Guideline page.