This is my collection of useful resources for learning robotics online. I will try my best to organize the materials (from generic to specific, fundamental to state-of-the-art) and hopefully update them frequently.
I will start with some online courses first (video lectures available).
Fundamentals
MIT - Linear Algebra by Gilbert Strang is a good starting class.
Harvard - Probability by Joe Blitzstein.
HMC - Real Analysis by Francis Su.
CUHK - Information Theory by Raymond W. Yeung.
Stanford - Convex Optimization by Stephen Boyd.
Harvard - Introduction to Computer Science by David J. Malan.
MIT - Introduction to Algorithms by Erik Demaine and Srini Devadas.
Upenn - Robotics by Vijay Kumar, CJ Taylor, Daniel E. Koditschek, Daniel Lee, Jianbo Shi, Kostas Daniilidis, Sid Deliwala.
Stanford - Machine Learning by Andrew Ng.
MIT - Artifical Intelligence by Patrick Henry Winston.
Basics
Stanford - Numpy by Justin Johnson.
Umich - Control with matlab by Bill Messner and Dawn Tilbury.
ETHZ - ROS by Péter Fankhauser, Dominic Jud, Martin Wermelinger and Marco Hutter.
Freiburg - C++ or Udacity - C++ or TheChernoProject - C++
Robot Mechanics
SNU - Robot Mechanics and Control by Frank C. Park (EiC of T-RO).
NU - Modern Robotics by Kevin Lynch (EiC of T-RO).
Dynamical Systems and Controls
Stanford - Linear Dynamical Systems by Stephen Boyd.
Shef - Modelling and Control by John Anthony Rossiter.
MIT - Nonlinear Systems by Jean-Jacques Slotine.
SNU - Nonlinear System Theory by Hyungbo Shim.
Motion Planning
NTU - Open-Source Robotics by Quang-Cuong Pham and Francisco Suárez-Ruiz.
Localization and Mapping
Optimal Control
Shef - Predictive control for beginners by John Anthony Rossiter.
MIT - Underactuated by Russ Tedrake, 2018.
Tsinghua - Dynamic Programming by Dimitri P. Bertsekas.
Artificial Intelligence
Cambridge - Information Theory, PR & NN by David MacKay.
MIT - Deep Learning for Self-Driving Cars by Lex Fridman, 2018.
Stanford - CNN for Visual Recognition by Fei-Fei Li and her students.
Fast.ai - Deep Learning for Coders by Jeremy Howard and Rachel Thomas.
UCL - Reinforcement Learning by David Silver, 2015.
Berkeley - Deep Reinforcement Learning by Sergey Levine, 2018.
MIT - Artifical General Intelligence by Lex Fridman, 2018.
Berkeley - Deep RL Bootcamp by Pieter Abbeel et. al, 2017.
