The Master of Science in Artificial Intelligence (MSAI) is a professional degree that prepares engineers, applied scientists and technical professionals for career advancement in advanced technical and technical leadership roles in the rapidly growing field of artificial intelligence engineering. The core curriculum addresses a breadth of areas central to AI engineering expertise including machine learning, statistical learning, data mining and ethics.
Program Policies
This CU Boulder on Coursera program does not align with standard campus policies. Please refer to the Online Programs section of the catalog for more information.
Requirements
The MSAI on Coursera is a non-thesis degree program that requires 30 credit hours of graduate-level coursework. This includes 15 credits of required breadth coursework and a choice of 15 credits hours of elective coursework from the options listed below. Students must complete 5 elective specializations or a combination of 4 complete elective specializations and three 1-credit elective courses totaling 15 credits.
Outside Electives
Up to 6 graduate-level credit hours of courses offered by other CU degrees on Coursera may be applied as elective credits toward the MSAI 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 ME-EM, MS-DS, and MS-EE programs on Coursera that do not start with a "CSCA" prefix, with the exception of the following courses. Credit from these courses cannot be applied 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 /CSCA 5812
Cross-listed courses do not count as outside electives. Note that courses cross-listed with the MS-AI electives listed below are not considered outside electives and do not count against this six-credit limit.
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 particular course toward two graduate certificates or two graduate degrees. If you want to complete degrees in more than one program, you must complete all the requirements for both degrees with no shared or overlapping course work. CU certificates on Coursera are automatically conferred once all requirements are met.
The MSAI on Coursera requires a minimum cumulative GPA of 3.00 and a grade of B or better in each breadth class (including the two required pathway specializations and the three additional required breadth specializations). Courses in which grades below C (2.0) are received may not be applied toward degree requirements.
| Code | Title | Credit Hours |
|---|---|---|
| Breadth Courses (required) | 15 | |
| Take all 5 complete specializations listed below, for a total of 15 credits. | ||
| Machine Learning (Pathway Specialization) | ||
| Introduction to Machine Learning: Supervised Learning | ||
| Unsupervised Algorithms in Machine Learning | ||
| Introduction to Deep Learning | ||
| Foundations of Probability and Statistics (Pathway Specialization) | ||
| Probability Foundations for Data Science and AI | ||
| Discrete-Time Markov Chains and Monte Carlo Methods | ||
| Statistical Estimation for Data Science and AI | ||
| Introduction to Artificial Intelligence | ||
| Intelligent Agents and Search Algorithms | ||
| Knowledge Representation and Reasoning Under Uncertainty | ||
| Introduction to Learning | ||
| Artificial Intelligence Ethics | ||
| Current Issues in Ethics and AI | ||
| AI Regulation | ||
| AI and the Future of Society | ||
| Reinforcement Learning | ||
| Mastering Classic Reinforcement Learning Algorithms | ||
| Deep Reinforcement Learning: From Theory to Practice | ||
| Reward Programming: Optimizing RL Efficiency and Safety | ||
| Electives | 15 | |
| Choose five specializations from the options below or combination of four complete specializations and three 1-credit courses totaling 15 credits | ||
| Data Mining Foundations and Practice | ||
| Data Mining Pipeline | ||
| Data Mining Methods | ||
| Data Mining Project | ||
| Natural Language Processing | ||
| Fundamentals of Natural Language Processing | ||
| Deep Learning for Natural Language Processing | ||
| Model and Error Analysis for Natural Language Processing | ||
| Robotics | ||
| Basic Robotic Behaviors and Odometry | ||
| Robotic Mapping and Trajectory Generation | ||
| Robotic Path Planning and Task Execution | ||
| Fair Machine Learning | ||
| Fair Machine Learning: Foundations | ||
| Fair Machine Learning: Algorithms | ||
| Fair Machine Learning: Applications | ||
| Brains & Machines: Modeling Intelligence with Neural Networks | ||
| Foundations of Computational Neuroscience and Neural Models | ||
| Learning, Inference, and Neural Data Analysis | ||
| Advanced Topics in Neuroscience-Inspired AI | ||
| Computer Vision | ||
| Introduction to Computer Vision | ||
| Deep Learning for Computer Vision | ||
| Modern AI Models for Vision and Multimodal Understanding | ||
| Generative AI | ||
| Introduction to Generative AI | ||
| Modern Applications of Generative AI | ||
| Advances in Generative AI | ||
| Software Architecture for Big Data | ||
| Fundamentals of Software Architecture for Big Data | ||
| Software Architecture Patterns for Big Data | ||
| Applications of Software Architecture for Big Data | ||
| Network Systems: Principles and Practice (Linux and Cloud Networking) | ||
| Network Systems Foundation | ||
| Network Principles in Practice: Linux Networking | ||
| Network Principles in Practice: Cloud Networking | ||
| Linux System Administration | ||
| Users, Permissions and Command Line Use | ||
| Installing and Maintaining Software and Hardware | ||
| Networking and Security | ||
| Computing, Ethics, and Society | ||
| Computing, Ethics, and Society Foundations | ||
| Ethical Issues in AI and Professional Ethics | ||
| Ethical Issues in Computing Applications | ||
| Security and Ethical Hacking | ||
| Security and Ethical Hacking: Attacking the Network | ||
| Security and Ethical Hacking: Attacking Unix and Windows | ||
| Security and Ethical Hacking: Attacking Web and AI Systems | ||
| Foundations of Data Structures and Algorithms | ||
| Dynamic Programming, Greedy Algorithms | ||
| Approximation Algorithms and Linear Programming | ||
| Advanced Data Structures, RSA and Quantum Algorithms | ||
| Object-Oriented Analysis & Design | ||
| Object-Oriented Analysis and Design: Foundations and Concepts | ||
| Object-Oriented Analysis and Design: Patterns and Principles | ||
| Object-Oriented Analysis and Design: Practice and Architecture | ||
| Internet Policy: Principles and Problems | ||
| When to Regulate? The Digital Divide and Net Neutrality | ||
| Protecting Individual Privacy on the Internet | ||
| Cybersecurity in Crisis: Information and Internet Security | ||
| Foundations of Autonomous Systems | ||
| Modeling of Autonomous Systems | ||
| Requirement Specifications for Autonomous Systems | ||
| Verification and Synthesis of Autonomous Systems | ||
| Introduction to Human-Computer Interaction | ||
| Ideating and Prototyping Interfaces | ||
| User Interface Testing and Usability | ||
| Emerging Topics in HCI: Designing for VR, AR, AI | ||
| Standalone Electives | ||
| Fundamentals of Data Visualization | ||
Learning Outcomes
Through the completion of the program, students will be able to:
- Demonstrate an understanding of the mathematical and computational foundations of AI.
- Design state of the art AI techniques to solve problems of relevance to industry and society at large.
- Use existing AI tools and techniques with an expert understanding of the principles behind their design and operation, and advance new AI tools and techniques to push the boundaries of AI.
- Apply AI techniques to diverse areas including healthcare, finance, education, engineering design and government.
- Keep up with the evolution of AI technology and maintain a lifelong professional readiness to adapt with the changing technology landscape.
- Appreciate the ethical implications of AI technology and the potential pitfalls behind specific deployments of AI techniques.