Artificial Intelligence, Master of Science
The Master of Science in Artificial Intelligence curriculum is designed to prepare graduates with the foundation, principles, and technical knowledge, to master the comprehensive framework of theory and practice in the emerging field of AI. Students will gain technical skills and practical expertise for developing and deploying ethical and human-centered artificial intelligence techniques to real-world applications.
The core courses provide comprehensive foundations and practical applications. Students take courses in AI programming, discrete math and algorithms, machine learning and deep learning, natural language processing, agent-based systems, cloud computing, among other courses, and cover special topics before the applied capstone project that simulates real-world experience.
Program Outcomes
This program will prepare students to:
1. Apply foundational knowledge, principles, and practices of artificial
intelligence (General Artificial Intelligence Knowledge).
2. Implement artificial intelligence principles and applied practices to use cases
(Artificial Intelligence Principles and Practices).
3. Analyze critical and ethical thinking to solve problems in artificial intelligence (Critical and Ethical Thinking).
4. Evaluate data to inform decisions and solve problems in artificial intelligence (Quantitative Literacy).
5 Create the ability to develop and express ideas while applying a variety of delivery modes, genres, and communication styles (Communication).
6. Collaborate effectively on diverse teams to accomplish a common goal (Collaboration).
Admission Requirements
In addition to City University of Seattle's graduate admission requirements, found under Admissions in the catalog menu, applicants to this program must also meet the following requirements:
- An earned bachelor's degree and evidence of completion of undergraduate courses or their equivalent in:
- Equivalency of 5-quarter hour credits at the intermediate level in at least one computer programming language; and
- Equivalency of 5-quarter hour credits in networking (TCP/IP from physical through applications layers); and
- Equivalency of 5-quarter hour credits of data management including basic database design and SOL/NoSQL Queries; and
- Equivalency of 5-quarter hour credits of operating systems including OS theory, process management, and memory management; OR
- An earned bachelor's degree and successful completion of CityU's Undergraduate Certificate in Foundations of Systems Development.
Total Required Credits (39-59 Credits)
Preparatory Courses (6 or 20 Credits)
Preparatory courses may be required for students entering the MSAI degree program without sufficient related experience in programming, SQL/NO SQL databases, networking, and operating systems. The breadth-first preparatory courses are recommended for students who want to study a broad spectrum of all topics within 1 quarter. The depth-first preparatory courses are recommended for students who want to study each topic in depth. Please see the program admissions criteria in the City University of Seattle catalog for specific information.
Breadth-First Preparatory Courses (6 Credits)
| CS 11A | Technology & Computing Components I | 3 |
| CS 11B | Technology & Computing Components II | 3 |
Depth-First Preparatory Courses (20 Credits)
| CS 132 | Computer Science I | 5 |
| | (or) | |
| IS 201 | Fundamentals of Computing | 5 |
| CS 330 | Network Communications * | 5 |
| CS 340 | Operating Systems * | 5 |
| IS 360 | Database Technologies * | 5 |
Pre-Entry Requirement (0 Credit)
Students must take this course in the first quarter of enrollment. Students may take another program requirement concurrently.
| CS 500 | STC MS Orientation to Master's Programs | 0 |
Core Requirements (24 Credits)
| AI 500 | Artificial Intelligence Overview | 3 |
| AI 510 | Artificial Intelligence in Cloud Computing | 3 |
| AI 520 | Natural Language Processing for Artificial Intelligence | 3 |
| CS 506 | Programming for Computing | 3 |
| CS 622 | Discrete Math and Algorithms for Computing | 3 |
| DS 510 | Artificial Intelligence for Data Science | 3 |
| DS 620 | Machine Learning & Deep Learning | 3 |
| DS 623 | Math & Statistics for Data Science | 3 |
Depth of Study: Artificial Intelligence (6 Credits)
| AI 610 | Agent Based Systems | 3 |
| AI 620 | Emerging Topics in Artificial Intelligence | 3 |
Electives (6 Credits)
Students may select two electives from any graduate courses within the School of Technology & Computing or complete the internship after taking three CS 650 seminar courses for their internship preparation.
Seminar
Students can take three CS 650 seminar courses after taking 6 credit hours and before taking either AI 680 Artificial Intelligence Internship or AI 687 Artificial Intelligence Capstone. Each enrollment must be pre-approved by the Program Manager.
| CS 650A | Master's Seminar I in Special Technology * | 1 |
| CS 650B | Master's Seminar II in Special Technology * | 1 |
| CS 650C | Master's Seminar III in Special Technology * | 1 |
Internship
This course is repeatable for credit. Each enrollment must be pre-approved by the Program Manager.
| AI 680 | Artificial Intelligence Internship * | 3 |
Capstone (3 Credits)
| AI 687 | Artificial Intelligence Capstone * | 3 |
Courses with an asterisk (*) after their title have a pre- or co-requisite.