The graduate certificate in Artificial Intelligence (AI) provides students a strong foundation in key AI topics. Students apply Machine Learning (ML) algorithms to real-world data sets; examine ethical issues in the design and implementation of current and future computing systems and technologies; create an appreciation for the tight interplay between mechanism, sensor and control in the design of robotic and intelligent systems; and study vital topics in generative AI reinforcement learning, natural language processing and autonomous systems.
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 AI graduate certificate requires 12 credit hours of coursework, made up of 1 credit of a required ethics course and 11 credits of the remaining course choices. To earn the certificate, students must complete three full specializations and an additional 2 courses, which can be made up of courses from different specializations. At least one of the 12 credits must be from the computing, ethics and society or artificial intelligence ethics specializations. Students can choose from the following courses and must receive a grade of C or higher in each course and a 3.0 cumulative GPA. Each course in a specialization is 1 credit.
| Code | Title | Credit Hours |
|---|---|---|
| Ethics Requirement | ||
| Choose 1 course from the following courses to fulfill the ethics requirement. | 1 | |
| Computing, Ethics, and Society | ||
| Computing, Ethics, and Society Foundations | ||
| Ethical Issues in AI and Professional Ethics | ||
| Ethical Issues in Computing Applications | ||
| Artificial Intelligence Ethics | ||
| Current Issues in Ethics and AI | ||
| AI Ethics and Society's Future | ||
| AI Ethics and Policy | ||
| Code | Title | Credit Hours |
|---|---|---|
| Specializations | ||
| Choose 3 complete specializations (9 courses) plus an additional 2 courses, which can be made up of courses from different specializations. | 11 | |
| Computing, Ethics and Society | ||
| Computing, Ethics, and Society Foundations | ||
| Ethical Issues in AI and Professional Ethics | ||
| Ethical Issues in Computing Applications | ||
| Artificial Intelligence Ethics | ||
| Current Issues in Ethics and AI | ||
| AI Ethics and Society's Future | ||
| AI Ethics and Policy | ||
| Natural Language Processing: Deep Learning Meets Linguistics | ||
| Fundamentals of Natural Language Processing | ||
| Deep Learning for Natural Language Processing | ||
| Model and Error Analysis for Natural Language Processing | ||
| Artificial Intelligence | ||
| Intelligent Agents and Search Algorithms | ||
| Knowledge Representation and Reasoning Under Uncertainty | ||
| Introduction to Learning | ||
| Reinforcement Learning | ||
| Reward Programming: Optimizing RL Efficiency and Safety | ||
| Deep Reinforcement Learning: From Theory to Practice | ||
| Mastering Classic Reinforcement Learning Algorithms | ||
| Foundations of Autonomous Systems | ||
| Modeling of Autonomous Systems | ||
| Requirement Specifications for Autonomous Systems | ||
| Verification and Synthesis of Autonomous Systems | ||
| Introduction to Robotics with Webots | ||
| Basic Robotic Behaviors and Odometry | ||
| Robotic Mapping and Trajectory Generation | ||
| Robotic Path Planning and Task Execution | ||
| Generative AI | ||
| Introduction to Generative AI | ||
| Modern Applications of Generative AI | ||
| Advances in Generative AI | ||
| Machine Learning | ||
| Introduction to Machine Learning: Supervised Learning | ||
| Unsupervised Algorithms in Machine Learning | ||
| Introduction to Deep Learning | ||
Learning Outcomes
- Gain a deep knowledge of AI, machine learning theory and its numerous applications including (but not limited to) natural language processing, computer vision, robotics, healthcare and human-centered computing.
- Design and implement comprehensive solutions for practical problems that incorporate the latest AI techniques.
- Identify the ethical implications in the design and application of AI technology and contribute to the emerging discussion in these areas as ethical developers of new technologies.
- Understand CS foundations, probability/statistics, programming languages and computer systems. Specifically, their knowledge will extend to how ideas from these sub-disciplines of CS support AI systems and vice-versa.
- Keep up with the state-of-the-art methods and techniques in this rapidly changing discipline of AI. Students will read and comprehend research papers and consider how the ideas in them can be applied in their everyday practice.