¾«Æ·SMÔÚÏßӰƬ

The MS-AI on Coursera is a non-thesis degree program that requires 30 credit hours of graduate-level coursework. This includes 15 credits of required Breadth courses, including the Pathway courses, and a choice of 15 Elective credits. Students must either complete 5 Elective specializations or a combination of 4 complete Elective specializations and three 1-credit Electives totaling 15 credits.

Each course is one credit and most courses are arranged in 3-course specializations. These specializations cover the same content that a 3-credit, 16-week course would cover. Current students can verify their degree requirements and degree progress in their degree audit in the .

Keep in mind:

• You may complete courses in any order.
• You do not need to wait to be admitted to take more courses and make progress on your degree.
• When you complete all three courses in one pathway with a B or better in each course, you are automatically admitted after the session you completed the Pathway courses.
• Credits you earn before admission will apply toward the degree.
• You must earn a B or better in your Breadth courses, and C or better in your Electives courses for credit toward your degree.
• Courses with grades below these minimums will not count toward your degree, but they will apply to your GPA.
• Students are required to maintain a minimum cumulative GPA of 3.00.
• Students may retake any course they want, but you can only repeat the same course once.
• This program qualifies for grade replacement.
• Courses may not be double-counted toward two credentials of the same level. This means students can apply credit from a particular course toward one graduate certificate and one graduate degree, but they cannot apply credit from a one course toward two graduate certificates or two graduate degrees.

The MS-AI on Coursera uses performance-based admissions, which means students earn program admission simply by performing well in a three-course Pathway specialization. To be admitted to the program, students enroll in and complete their preferred three-course Pathway specialization with a grade of B or better in each of the three courses, have a cumulative GPA of at least 3.00 for all for-credit courses taken to date, and declare intent to seek the degree via the enrollment form. Pathway courses are a required part of the curriculum, which means students make direct progress toward the degree while they work toward program admission.

There is no traditional application for admission to the degree. The ¾«Æ·SMÔÚÏßӰƬ never asks for transcripts, previous test scores (like GRE or TOEFL), application essays, letters of recommendation, or application fees. A prior degree is not required for admission. Because this program is fully online, students do not need to complete a background check to enroll.

The Master of Science in Artificial Intelligence (MS-AI) program hosted online through the Coursera platform offers stackable graduate-level courses and a fully accredited master’s degree in artificial intelligence. MS-AI on Coursera students earn the same credentials as on-campus students. There are no "online" or "Coursera" designations on official CU transcripts or diplomas.

The Department of Computer Science has embraced this degree as an ideal opportunity to expand access to the excellent graduate-level courses offered by the department's highly reputed faculty beyond ¾«Æ·SMÔÚÏßӰƬ's physical campus. The goal of the MS-AI on Coursera program is to produce creative, workforce-ready graduates equipped with versatile specialized skills and technical leadership.

Students pursuing this degree will also have access to a wide range of courses taught as part of other ¾«Æ·SMÔÚÏßӰƬ degrees offered on the Coursera platform, including topics such as data science, computer science, engineering management, and electrical engineering.

We highly recommend that students start all of their courses in the non-credit, open, version in Coursera. When you work on the course in the public (not-for-credit) version, you can work at your own pace and redo assignments. Then, when you are ready to enroll for credit, you use the enrollment form, pay tuition and complete an onboarding course, then the progress you made on the coursework in the public version will transfer into the for-credit version. Then, you will access the restricted for-credit content (usually a final exam or project) and complete the requirements by the session deadlines for credit.

Read more about the Curriculum and Courses in the next sections.

Complete ONE Pathway specialization with a B or better in each course for admission.

Complete BOTH pathways for the degree. Both of the Pathway specializations are part of the Breadth requirement for the degree.

Pathway | Breadth: Machine Learning: Theory & Hands-On Practice with Python SpecializationÌý(3 credits)

• CSCA 5622: Introduction to Machine Learning:ÌýSupervised LearningÌý(1 credit) – Cross-listed with DTSA 5509
• CSCAÌý5632: Unsupervised Algorithms in Machine LearningÌý(1 credit) – Cross-listed with DTSA 5510
• CSCA 5642: Introduction to Deep LearningÌý(1 credit) – Cross-listed with DTSA 5511

Pathway | Breadth: Statistical Learning Specialization (3 credits)
This specialization is currently in development.

• Statistical Learning Course 1 (1 credit)
• Statistical Learning Course 2 (1 credit)
• Statistical Learning Course 3 (1 credit)

There are 15 required Breadth courses, including the Pathway Breadth courses. Once you complete a Pathway Breadth specialization with a B or better in each course, you are admitted to the program.

To avoid confusion, we will not provide estimated course release dates. Confirmed release dates will be posted next to course titles when available.

Pathway | Breadth: Machine Learning: Theory & Hands-On Practice with Python SpecializationÌý(3 credits)

• CSCA 5622: Introduction to Machine Learning:ÌýSupervised Learning (1 credit)Ìý– Cross-listed with DTSA 5509
• CSCAÌý5632: Unsupervised Algorithms in Machine Learning (1 credit)Ìý– Cross-listed with DTSA 5510
• CSCA 5642: Introduction to Deep Learning (1 credit)Ìý– Cross-listed with DTSA 5511

Pathway | Breadth: Statistical Learning Specialization (3 credits)
This specialization is currently in development.

• Statistical Learning Course 1 (1 credit)
• Statistical Learning Course 2 (1 credit)
• Statistical Learning Course 3 (1 credit)

Breadth:

Computing, Ethics, and Society Specialization (3 credits)
This specialization is currently in development.

• CSCA 5214: Computing, Ethics, and Society Foundations (1 credit) - Cross-listed with MS-CS
• CSCA 5224: Ethical Issues in AI and Professional Ethics (1 credit) - Cross-listed with MS-CS
• CSCA 5234: Ethical Issues in Computing Applications (1 credit) - Cross-listed with MS-CSÌý

Artificial Intelligence Specialization (3 credits, 3 courses)
This specialization is currently in development.

• Artificial Intelligence Course 1 (1 credit)
• Artificial Intelligence Course 2 (1 credit)
• Artificial Intelligence Course 3 (1 credit)

Foundations of Reinforcement Learning Specialization (3 credits, 3 courses)
This specialization is currently in development.

• Foundations of Reinforcement Learning Course 1 (1 credit)
• Foundations of Reinforcement Learning Course 2 (1 credit)
• Foundations of Reinforcement Learning Course 3 (1 credit)

Select 15 Elective credits, including at least four full specializations.

• You may choose to complete five specializations or a combination of four specializations plus three 1-credit courses from different specializations.
• Up to six credits/2 specializations from other ¾«Æ·SMÔÚÏßӰƬ degrees on Coursera can be applied toward MS-AI elective credit requirements. See Outside ElectivesÌýbelow for details.
• To avoid any confusion we will not provide estimated release timelines of courses/specializations that are in development.
• NOTE: Any new or existing CSCA electives will count toward all catalog year degree requirements.

Bayesian Statistics/Graphical Models Specialization (3 credits)
This specialization is currently in development.

• Bayesian Statistics/Graphical Models Course 1 (1 credit)
• Bayesian Statistics/Graphical Models Course 2 (1 credit)
• Bayesian Statistics/Graphical Models Course 3 (1 credit)

Data Mining Foundations and Practice Specialization (3 credits)

• CSCA 5502:ÌýData Mining PipelineÌý(1 credit) – Cross-listed with DTSA 5504
• CSCA 5512:ÌýData Mining MethodsÌý(1 credit) – Cross-listed with DTSA 5505
• CSCA 5522:ÌýData Mining ProjectÌý– Cross-listed with DTSA 5506Ìý

Introduction to Robotics with Webots Specialization (3Ìýcredits)

• CSCAÌý5312:ÌýBasic Robotic Behaviors and Odometry (1 credit) Ìý- Cross-listed with MS-CS
• CSCAÌý5332: Robotic Mapping and Trajectory Generation (1 credit) - Cross-listed with MS-CS
• CSCAÌý5342:ÌýRobotic Path Planning and Task Execution (1 credit) - Cross-listed with MS-CSÌý

Natural Language Processing: Deep Learning Meets Linguistics (3Ìýcredits)
This specialization is currently in development.

• CSCA 5832: Fundamentals of Natural Language Processing (1 credit) – Cross-listed with DTSA 5747
• CSCA 5842: Deep Learning for Natural Language Processing (1 credit) – Cross-listed with DTSA 5748
• CSCA 5852: Model and Error Analysis for Natural Language Processing (in development) (1 credit) – Cross-listed with DTSA 5749

Generative AI Specialization (3 credits)
This specialization is currently in development.

• CSCA 5112: Introduction to Generative AI (1 credit)
• CSCA 5122: Modern Applications of Generative AI (in development) (1 credit)
• CSCA 5132: Advances in Generative AIÌý(in development) (1 credit)

Introduction to Computer Vision (3 credits)
This specialization is currently in development.

• CSCA 5222: Introduction to Computer Vision (1 credit)
• CSCA 5322: Deep Learning for Computer Vision (1 credit)
• CSCA 5422: Computer Vision for Generative AI (1 credit)

Deep Learning SpecializationÌý(3 credits, 3 courses)
This specialization is currently in development.

• Deep Learning Course 1 (1 credit)
• Deep Learning Course 2 (1 credit)
• Deep Learning Course 3 (1 credit)

Optimization SpecializationÌý(3 credits, 3 courses)
This specialization is currently in development.

• Optimization Course 1 (1 credit)
• Optimization Course 2 (1 credit)
• Optimization Course 3 (1 credit)

Recommender SystemsÌýSpecialization (3 credits, 3 courses)
This specialization is currently in development.

• Recommender Systems Course 1 (1 credit)
• Recommender Systems Course 2 (1 credit)
• Recommender Systems Course 3 (1 credit)

Text Mining SpecializationÌý(3 credits, 3 courses)
This specialization is currently in development.

• Text Mining Course 1 (1 credit)
• Text Mining Course 2 (1 credit)
• Text Mining Course 3 (1 credit)

Ìý

You can apply up to six graduate-level credit hours/2 specializations of courses offered by other CU degrees on Coursera toward the MS-AI on Coursera degree*. All courses must be graduate level, offered through Coursera, and meet all applicable academic standards. This includes all courses offered by the MS-CS on Coursera, ME-EM on Coursera, the MS-DS on Coursera, and the MS-EE on Coursera programs except the following courses.

*Admitted students will receive an email regarding Outside Electives at the end of every session after grades post. This email has the form to give the program permission to apply your outside electives to your degree. Students must earn a C grade or better to apply outside electives.
Ìý
You cannot apply credit from the following courses toward MS-AI on Coursera requirements:

• DTSA 5302 Cybersecurity for Data Science
• DTSA 5303 Ethical Issues in Data Science
• DTSA 5501 Algorithms for Searching, Sorting, and Indexing
• DTSA 5502 Trees and Graphs: Basics
• DTSA 5707 Deep Learning Applications for Computer Vision

If you wantÌýto complete degrees in more than one program, you must complete all the requirements for both degrees with no shared or overlapping course work.