This is CMSC389F, the University of Maryland's theoretical introduction to the art of reinforcement learning. An introductory course taught by Kevin Chen and Zack Khan, CMSC389F covers topics including markov decision processes, monte carlo methods, policy gradient methods, exploration, and application towards real environments in broad strokes .
Through my PhD on Deep Learning based robotics, I read a lot of papers on Machine Learning, Reinforcement Learning and AI in general. But papers can be a bit...
These are lectures for course 6.S094: Deep Learning for Self-Driving Cars taught in Winter 2017. Course website: http://cars.mit.edu Contact: deepcars@mit.ed...