Applications of Machine Learning

Related lectures (31)

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Covers learning and adaptive control for robots, focusing on real-time reactivity and path planning using dynamical systems.

Explores the Weibull model's application to random data and its significance in analyzing material strength and failure probability.

Covers challenges and solutions for robots to work safely with humans, emphasizing adaptability and predictability.

Covers the statistical distribution in canonical and grand-canonical ensembles, focusing on the bathed system.

Explores obstacle avoidance using Dynamical Systems for robots, focusing on modulation, stability guarantees, and contraction theory.

Explores learning and adaptive control for robots through SEDS and LPV-DS, emphasizing stability, non-linear dynamics, and optimization.

Explores data-driven approaches to improve robot design, focusing on compliance, soft materials, and complex interactions.

Delves into the definition and capabilities of robots based on various sources.

Explores learning and adaptive control for robots, emphasizing modulation of dynamical systems to improve stability and enable reactive motion.

Explores pulse techniques for signal measurements and noise reduction methods like gated integration and boxcar averaging.