Program Overview

AI Graduate Certificate

Overview

The AI Graduate Certificate requires 12 credits to be completed in 4 full specializations. A specialization is a grouping of 3 courses in a topic. Each course is 1 credit. 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.

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.
• Students will be able to design and implement comprehensive solutions for practical problems that incorporate the latest AI techniques.
• Students will be able to 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 that research can be applied in their everyday practice.

Recommended Prior Knowledge

There are no formal prerequisites, but we recommend that you have prior knowledge of basic mathematical concepts and computer programming.

• Math: Calculus, Discrete Mathematics, Probability and Statistics and Linear Algebra
• Programming: Python and R Programming

Curriculum & Requirements

Students will select any 4 specializations in the section to fulfill the 12 credit, 4 specializations requirement.

• Computing, Ethics, and Society Specialization
  • CSCA 5214: Computing, Ethics, and Society Foundations
  • CSCA 5224: Ethical Issues in AI and Professional Ethics
  • CSCA 5234: Ethical Issues in Computing Applications
• Machine Learning: Theory and Hands-On Practice with Python Specialization
  • CSCA 5622: Introduction to Machine Learning: Supervised Learning – Cross-listed with DTSA 5509
  • CSCA 5632: Unsupervised Algorithms in Machine Learning – Cross-listed with DTSA 5510
  • CSCA 5642: Introduction to Deep Learning – Cross-listed with DTSA 5511
• Foundations of Autonomous Systems Specialization
  • CSCA 5834: Modeling of Autonomous Systems
  • CSCA 5844: Requirement Specifications for Autonomous Systems
  • CSCA 5854: Verification and Synthesis of Autonomous Systems
• Introduction to Robotics with Webots Specialization
  • CSCA 5312: Basic Robotic Behaviors and Odometry
  • CSCA 5332: Robotic Mapping and Trajectory Generation
  • CSCA 5342: Robotic Path Planning and Task Execution
• Generative AI Specialization
  • CSCA 5112: Introduction to Generative AI
  • CSCA 5122: Modern Applications of Generative AI (in development)
  • CSCA 5132: Advances in Generative AI (in development)
• Natural Language Processing: Deep Learning Meets Linguistics Specialization
  • CSCA 5832: Fundamentals of Natural Language Processing
  • CSCA 5842: Deep Learning for Natural Language Processing
  • CSCA 5852: Model and Error Analysis for Natural Language Processing
• Artificial Intelligence Specialization
  • CSCA 5002: Intelligent Agents and Search Algorithms (1 credit)
  • CSCA 5012: Knowledge Representation and Reasoning Under Uncertainty (1 credit)
  • CSCA 5022: Introduction to Learning (1 credit)
• Reinforcement Learning Specialization
  • CSCA 5902: Mastering Classic Reinforcement Learning Algorithms (1 credit)
  • CSCA 5912: Deep Reinforcement Learning: From Theory to Practice (1 credit)
  • CSCA 5922: Reward Programming: Optimizing RL Efficiency and Safety (1 credit)

Financing

The Artificial Intelligence Graduate Certificate is $525 per credit hour. The program requires 12 credit hours of coursework, the total program cost is $6,300.

Earn Credit towards a Master's Degree

This Graduate Certificate can be stacked toward the Master of Science in Computer Science (MS-CS) on Coursera degree if you are interested in continuing your education.

FAQ

• How can I stack this certificate toward an MS-CS degree? Students in the MS-CS program can automatically earn the AI graduate certificate. Alternatively, if you select the specializations for your AI certificate, you will have completed 40% of your MS-CS degree.
• Can I earn the MS-CS degree, the DS Certificate AND the AI Certificate with only 30 credits? Not yet. Because Machine Learning is a required Breadth specialization for the MS-CS degree and you cannot apply the same credits to 2 of the same level of credentials, so you will need to apply the Machine Learning specialization to the AI certificate if you plan to earn the MS-CS degree, the DS certificate and the AI certificate.
• Will this certificate help me in my career? Yes! The AI Graduate Certificate prepares engineers, applied scientists, and technical professionals for career advancement in advanced technical and technical leadership roles. Curriculum addresses a range of areas including theory, software, systems, machine learning, and ethics.
• Can I earn this certificate if I have already completed my MS-CS degree? Yes! If a student was to earn an MS-CS degree and they want to come back after they earn a degree to pursue additional classes towards a MS-CS certificate (or other CU on Coursera certificate), they can! The student can re-enroll in classes through the current student enrollment form and it will add them to a 2nd non-degree program plan and if they meet requirements for a certificate, it will be added at the end of each term.