MS in Artificial Intelligence

Market research has demonstrated that there are few fields with as much projected growth over the next 10 years as artificial intelligence. With increased globalization of artificial intelligence and worldwide collaboration efforts for technology development, it is expected that students graduating with this degree will have a good opportunity for finding high-level research engineering positions in different industries in Michigan, across the United States, as well as anywhere else in the world. The program will also allow students to build their technical skills and to further understand the complex human systems in which they will be implemented.

The program requires 16 semester hours of foundation and core courses and 16 semester hours of depth/elective course work for a total of 32 semester hours. Professional track students are required to take at least 3 depth courses. Research track students are required to take Master’s Thesis Research plus at least 2 depth courses.

Option 1: Professional Track. This option requires students take only courses, which may include an independent study, based on the preferences of the student. The minimum requirements are as follows:
Core courses – 12 credit hours
Depth/concentration courses – 12 credit hours
Elective courses – 8 credit hours

Option 2: Research Track. This option requires a research thesis or project prepared under the supervision of the advisor. The thesis or project 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 are as follows:
Core courses – 12 credit hours
Depth/concentration courses – 12 credit hours
Master’s Thesis/Project and/or Research/seminar Courses — 8 credit hours

Core Courses

All students, regardless of whether they are on the professional or research track, must take 12 credits of core courses, including:

• CSI 5130 – Artificial Intelligence (4 credits)
• CSI 5170 – Machine Learning (4 credits)
• CSI 5390 – Software Engineering OR CSI 5220 – Object Oriented Analysis Design (4 credits)

Depth Courses

Students must declare one concentration from which to take 12 credits of depth courses. Each concentration has one required core course, and two elective courses.

Core course (required)
CSE 5100 – Foundations of Edge AI (4 credits) new!

Electives (choose 2)
• CSE 5240 – Cloud Computing (4 credits)
• CSE 5490 – Wireless and Industrial Networks (4 credits)
• CSE 6470 – Advanced Computer Networks (4 credits)
• CSE 6480 – Information Security (4 credits)
• CSI 5140 – Deep Learning and Applications (4 credits)
• CSI 5230 – Mobile and Smart Phone Application Development (4 credits)
• ECE 6212 – Wireless Communications (4 credits)

Core course (required)
ECE 4900 – ST: Embedded Artificial Intelligence (4 credits)

Electives (choose 2)
• ECE 5520 – Automotive Mechatronics I (4 credits)
• ECE 5720 – Microprocessor-Based Systems Design (4 credits)
• ECE 5731 – Embedded Computing in Mechatronics (4 credits)
• ECE 5770 – GPU Accelerated Computing (4 credits)
• ECE 6410 – Intelligent Control Systems (4 credits)
• ECE 6520 – Automotive Mechatronics II (4 credits)
• ECE 6712 – Parallel Embedded Computer Architecture (4 credits)
• ECE 6742 – DSP in Embedded Systems (4 credits)
• ECE 6745 – Real-Time Computing Systems (4 credits)
• CSE 5360 – Concurrent and Multi-Core Programming (4 credits)
• CSE 5420 – Software Architecture and Components (4 credits)
• CSE 5170 – Pattern Recognition and Machine Learning (4 credits)

Core course (required)
CSI 5900 – AI-Human Interaction (4 credits)

Electives (choose 2)
• CSE 5550 – Visual Computing (4 credits)
• SYS 5900 – ST: Automotive User Experience (4 credits)
• CSE 5550 – Visual Computing (4 credits)
• CSE 6550 – Advanced Visual Computing (4 credits)
• CSI 5140 – Deep Learning and Applications (4 credits)
• ECE 5500 – Robotic Systems and Control (4 credits)
• ECE 5532 – Autonomous Vehicle Systems I (4 credits)
• ECE 6410 – Intelligent Control Systems (4 credits)
• ECE 6440 – Adaptive Control Systems (4 credits)
• ECE 6460 – Autonomous Vehicle Systems II (4 credits)
• ECE 6467 – Dynamics and Control of Robot Manipulators (4 credits)
• ISE 5422 – Robotic Systems (4 credits)
• ECE5551 – Human-Robot Interaction (4 credits)
• CSI 5180 – Natural Language Processing (4 credits)

Core course (required)
CSI 5140 – Deep Learning and Applications (4 credits)

Electives (choose 2)
• CSE 5170 – Pattern Recognition and Machine Learning (4 credits)
• CSE 5810 – Information Retrieval and Knowledge Discovery (4 credits)
• ME 5310 – Machine Learning Engineering Design (4 credits)
• ISE 5002 – Engineering Operations Research (4 credits)
• ISE 5430 – Engineering Operations Research – Deterministic Models (4 credits)
• ISE 5431 – Engineering Operations Research – Stochastic Models (4 credits)
• ISE 5517 – Statistical Methods in Engineering (4 credits)
• ME 5434 – Metamodeling and Optimization Methods in Design (4 credits)
• MIS 6900 ST: Deep Learning and Text Analytics (3 credits)
• CSI 5180 – Natural Language Processing (4 credits)

Core course (required)
ISE 5512 – Artificial Intelligence in Manufacturing (4 credits)

Electives (choose 2)
• SYS 5900 – ST: Virtual and Augmented Reality (4 credits)
• CSE 5240 – Cloud Computing (4 credits)
• CSE 6480 – Information Security (4 credits)
• ISE 5422 – Robotic Systems (4 credits)
• ISE 5456 – Engineering Risk Analysis (4 credits)
• ISE 5464 – Design for Manufacturing and Assembly Analysis (4 credits)
• ISE 5410 – Supply Chain Modeling and Analysis (4 credits)
• ISE 5002 – Engineering Operations Research (4 credits)
• ISE 5517 – Statistical Methods in Engineering (4 credits)
• CSE 5550 – Visual Computing (4 credits)

Core course (required)
CSE 5150 – AI for IT Operations (4 credits) new!

Electives (choose 2)
• CSE 5220 – Object Oriented Analysis and Design (4 credits)
• CSE 5200 – Fundamentals of Software Modeling (4 credits)
• CSE 5300 – Software Prototyping and Validation (4 credits)
• CSE 5380 – Software Verification and Testing (4 credits)
• CSE 5390 – Software Engineering (4 credits)
• CSE 5410 – Software Project Planning, Management and Maintenance (4 credits)
• CSE 5720 – Software Security (4 credits)
• CSI 5140 – Deep Learning and Applications (4 credits)

Core course (required)
CSI 5120 AI in Cybersecurity and Privacy (4 credits) new!

Electives (choose 2)
• CSI 5460 – Information Security (4 credits)
• CSI 5480 – Information Security Practices (4 credits)
• CSI 5720 – Software Security (4 credits)
• CSI 5140 – Deep Learning and Applications (4 credits)

Core course (required)
SYS 5900 – ST: Virtual and Augmented Reality (4 credits)

Electives (choose 2)
• SYS 5900 – ST: Automotive User Experience (4 credits)
• CSI 4900/5900 – AI-Human Interaction (4 credits) (4 credits)
• CSE 5550 – Visual Computing (4 credits)
• ECE 5551 – Human-Robot Interaction (4 credits)

Core course (required)
CSI 5100 – Ethics and Bias in AI (4 credits) new!

Electives (choose 2)
• CSI 5900 – AI-Human Interaction (4 credits) (4 credits)
• CSI 5140 – Deep Learning and Applications (4 credits)
• CSE 5170 – Pattern Recognition and Machine Learning (4 credits)
• CSE 5810 – Information Retrieval and Knowledge Discovery (4 credits)

Full catalog information will be available in summer ’23.

Questions? Contact us!
Dr. Hua Ming
Academic Programs Coordinator
Email: ming@oakland.edu

Preparation for the MS in AI

Regular admission to the program requires a bachelor’s degree in a science, technology, engineering, or mathematics (STEM) field earned with an average
of B (or better) from an accredited program. Students without prior technical knowledge may be required to complete a PACE noncredit certificate, or a foundational Graduate Certificate in Computer Science, to be eligible for admission in the MS in AI program. An entering student should have completed one course in probability and statistics, one course in programming, and one course in calculus II (see the table shown below). A course in calculus III and a course in linear algebra are recommended but not required.

Deficiencies in the prerequisites may be made up after entrance into the program. Students with such deficiencies must complete the missing prerequisite course(s) with a grade of “B” or better within the first two semesters after entering the program. Students without a computing background should first complete our foundational Graduate Certificate in Computer Science, which upon successful completion, can be combined with other certificates and applied toward a full AI master’s program. The foundational Graduate Certificate in Computer Science is composed of CSI 5390 – Software Engineering OR CSI 5220 – Object Oriented Analysis Design, CSE 5610 – Advanced Data Structures and Algorithms and CSI 5500 – Operating Systems I. Industry experience in programming and computing can be considered as part of the evaluation process and an interview can be conducted with the applicant to check their credentials.