Monthly Subscription Starting at AED 99 + VAT
- Level Foundation
- Duration 15 hours
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How can robots determine their state and properties of the surrounding environment from noisy sensor measurements in time? In this module you will learn how to get robots to incorporate uncertainty into estimating and learning from a dynamic and changing world. Specific topics that will be covered include probabilistic generative models, Bayesian filtering for localization and mapping.Auto Summary
Discover the foundational aspects of Robotics: Estimation and Learning with an engaging course designed for aspiring engineers and scientists. Guided by Coursera, this 900-minute course delves into probabilistic generative models and Bayesian filtering for effective localization and mapping. Ideal for beginners, the program offers a Starter subscription, perfect for those eager to understand how robots interpret and learn from noisy sensor data in a dynamic environment. Join now to incorporate uncertainty into your robotic systems and enhance their interaction with the ever-changing world.