Mechanical
Data-driven engineering, control, robotics and machine learning for physical systems.
Advanced
Once the basics are comfortable.
Convexity Implies that Local Minima are Global Minima
A video from Steve Brunton (University of Washington) on Convexity Implies that Local Minima are Global Minima. Best once you are comfortable with the basics of mechanical engineering. It plays here in the portal, and your progress is saved as you watch.
Steve Brunton (University of Washington)
Activation Functions in Machine Learning
A video from Steve Brunton (University of Washington) on Activation Functions in Machine Learning. Best once you are comfortable with the basics of mechanical engineering. It plays here in the portal, and your progress is saved as you watch.
Steve Brunton (University of Washington)
Bayesian Linear Regression [Python Example]
A video from Steve Brunton (University of Washington) on Bayesian Linear Regression [Python Example]. Best once you are comfortable with the basics of mechanical engineering. Includes worked code you can follow along with. It plays here in the portal, and your progress is saved as you watch.
Steve Brunton (University of Washington)
Bayesian Linear Regression and Maximum a Posteriori (MAP) Estimate
A video from Steve Brunton (University of Washington) on Bayesian Linear Regression and Maximum a Posteriori (MAP) Estimate. Best once you are comfortable with the basics of mechanical engineering. It plays here in the portal, and your progress is saved as you watch.
Steve Brunton (University of Washington)
Bayesian Linear Regression and Maximum Likelihood Estimates
A video from Steve Brunton (University of Washington) on Bayesian Linear Regression and Maximum Likelihood Estimates. Best once you are comfortable with the basics of mechanical engineering. It plays here in the portal, and your progress is saved as you watch.
Steve Brunton (University of Washington)
HydroGym: A Reinforcement Learning Platform for Fluid Dynamics
A video from Steve Brunton (University of Washington) on HydroGym: A Reinforcement Learning Platform for Fluid Dynamics. Best once you are comfortable with the basics of mechanical engineering. It plays here in the portal, and your progress is saved as you watch.
Steve Brunton (University of Washington)
Monte Carlo Sampling and Bootstrapping in Bayesian Inference
A video from Steve Brunton (University of Washington) on Monte Carlo Sampling and Bootstrapping in Bayesian Inference. Best once you are comfortable with the basics of mechanical engineering. It plays here in the portal, and your progress is saved as you watch.
Steve Brunton (University of Washington)
Machine Learning for Fluid Dynamics
A lecture series on where machine learning helps in fluid mechanics: flow modelling, turbulence closure, reduced-order models and flow control, with honest discussion of what it cannot yet do.
Steve Brunton (University of Washington)
Reinforcement Learning for Engineers
Reinforcement learning explained from an engineer's point of view: value functions, policy gradients, Q-learning and deep RL, linked back to optimal control.
Steve Brunton (University of Washington)
Expert
Research-level depth and specialist material.
Dynamical Systems with Machine Learning
Modelling how systems change over time, then discovering those models from data with methods such as SINDy, DMD and Koopman analysis. For students comfortable with differential equations.
Steve Brunton (University of Washington)
Physics-Informed Machine Learning
How to build machine learning models that respect the laws of physics: adding conservation laws, symmetries and known equations to neural networks so they work with less data and generalise better. A strong bridge between classical mechanics and modern ML.
Steve Brunton (University of Washington)