Modeling of Autonomous Systems
Key Takeaways
Models autonomous systems using state-space representations, timed automata, and hybrid automata
Original Description
This course will explain the core structure in any autonomous system which includes sensors, actuators, and potentially communication networks. Then, it will cover different formal modeling frameworks used for autonomous systems including state-space representations (difference or differential equations), timed automata, hybrid automata, and in general transition systems. It will describe solutions and behaviors of systems and different interconnections between systems.
This course can be taken for academic credit as part of CU Boulder’s MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more:
MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
Watch on External: Coursera ↗
(saves to browser)
Sign in to unlock AI tutor explanation · ⚡30
Related Reads
📰
📰
📰
📰
Agent-readiness, AEO and GEO: how they relate
Dev.to · Erik Rekola
How to Build an AI Agent for Real Estate Automation: The 2026 Production Playbook
Dev.to AI
GitLab 19.2 Puts AI Agents to Work on the Security Backlog
InfoQ AI/ML
NVIDIA Releases Cosmos 3 Edge: A 4B-Parameter Open World Model That Reasons and Generates Robot Actions On-Device
MarkTechPost
🎓
Tutor Explanation
DeepCamp AI