Here's a collection of various resources that I've found really helpful over the years:
Here's a collection of various resources that I've found really helpful over the years:
A micro Lie theory for state estimation in robotics (Joan Solà et al): Lecture video, Paper
Tom Drummond's notes
Ethan Eade's documents
Feedback in Machine Learning systems:
Short video [Drew Bagnell]
Longer ICML2020 talk [A Venkatraman, S Chaudhary]
Intro to AI (UC Berkeley CS188) - The Pac-Man projects are the best set of assignments I've ever done.
Statistical Techniques in Robotics (CMU Robotics 16-831) - A great overview of probabilistic and learning techniques in robotics.
Advanced Robotics (UC Berkeley CS 287) - Tour de force of Robotics. The lecture slides are fantastic reference material.
Introduction to Reinforcement Learning (David Silver)
Deep RL bootcamp (Pieter Abbeel et al)
This document by Prof Harchol at CMU