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Courses for Robotics

Introduction

There is a lot of ~~hidden~~ treasure lying scattered across the internet. This list is an attempt to bring to light those awesome online video courses related to robotics.

Table of Contents

  1. Basics of Robots
  2. Linear Algebra
  3. Optimization
  4. Signals and System
  5. Digital Signal Processing
  6. Controls
  7. Machine Learning
  8. Hybrid Courses (ML + Control)
  9. Motion Planning
  10. SLAM
  11. Probability
  12. Miscellaneous

Courses

Basics of Robotics

  1. Introduction to Robotics by Prof. Oussama Khatib, Stanford
  1. Programming for Robotics (ROS) by Prof. Marco Hutter, ETH Zurich
  1. Introduction to Linear Dynamical Systems by Prof. Stephen Boyd, Stanford University
  1. Advanced Robotics, Fall 2013 by Prof. Pieter Abbeel, UCB

Linear Algebra

  1. Advanced Matrix Theory and Linear Algebra for Engineers by Prof. R. Vittal Rao, IISc
  1. Linear Algebra by Prof. Gilbert Strang, MIT
  1. Linear Algebra by Prof. Lorenzo Sadun, UT Austin
  • youtube playlist
  • They way he solved wave equations is simple and amazing. Probably, best in the world!

Optimization

  1. Numerical Optimization by Prof. Shirish K. Shevade, IISc
  1. Convex Optimization: Fall 2018 by Dr. Ryan Tibshirani, CMU
  1. Advanced Optimization and Randomized Methods by Alex Smola and Suvrit Sra, CMU
  1. Optimization: Fall 2012 by Geoff Gordon and Ryan Tibshirani, CMU
  1. Convex Optimization I by Prof. Stephen Boyd, Stanford University
  1. Convex Optimization II by Prof. Stephen Boyd, Stanford University
  1. Monte Carlo Methods and Stochastic Optimization by Verena Kaynig-Fittkau and Pavlos Protopapas, Harvard
  1. Optimization Algorithms by Constantine Caramanis, UT Austin
  1. Computational Statistics

Signals and System

  1. Signals and Systems, Fall 2011 by Prof. Dennis Freeman, MIT
  1. Signals and Systems, 2012 by Prof. S.C. Dutta Roy, IIT Delhi
  1. Signals and Systems, 1987 by Prof. Alan V. Oppenheim, MIT
  1. Signal Processing by Prof. Barry Van Veen, University of Wisconsin - Madison

Other References

  1. David Doran's Youtube Channel
  2. Fourier Series slides

Digital Signal Processing

  1. DSP by Paolo Prandoni and Prof. Martin Vetterli, EPFL (Coursera)
  1. DSP, 2014 by Prof. S.C. Dutta Roy, IIT Delhi
  1. DSP, 1987 by Prof. Alan V. Oppenheim, MIT
  1. DSP by Rich Radke, RPI
  1. Digital Image Processing by Rich Radke, RPI
  1. Compressed Sensing and Sparse Signal Processing by IISc Prof. Chandra R. Murthy
  • course webpage
  • videos
  • Follows, "A Mathematical Introduction to Compressive Sensing" by Foucart Simon.

  1. Andrew Reader Medical Image Reconstruction

  2. Tsinghua Course on Sparse Approximation

Controls

  1. Control Bootcamp by Prof. Steve Brunton, UW
  1. Classical Control Theory by Brian Douglas
  1. Control Systems Engineering by Benjamin Drew
  1. Nonlinear Controls by Prof. Slotine, MIT
  1. Numerical Methods for Optimal Control by Prof. Sebastien Gross, NTNU
  1. Optimal and Robust Control by CTU in Prague
  1. State Space Control by Dr. Jake Abbott, Univ. of Utah

Machine Learning

  1. Learning from Data by Prof. Yaser Abu-Mostafa, Caltech
  1. Deep Learning by Lex Fridman, MIT
  1. Statistical Learning by prof. Trevor Hastie and prof. Rob Tibshirani, Stanford
  1. Artificial Intelligence by Prof. Patrick Winston, MIT
  1. Machine Learning by Prof. Andrew Ng, Stanford
  1. Introduction to Computational Thinking and Data Science, Fall 2016 by Prof. John Guttag, MIT
  1. Probabilistic Graphical Models by Chris Bishop, Microsoft Research Cambridge
  1. Machine Learning and Nonparametric Bayesian Statistics by prof. Zoubin Ghahraman, University of Cambridge
  1. Reinforcement Learning by David Silver, UCL

Hybrid Courses (ML + Control)

  1. Data-Driven Science and Engineering by Prof. Steve Brunton, UW
  1. Underactuated Robotics by Prof. Russ Tedrake, MIT

Motion Planning

  1. Robotic Motion Planning by Prof. Howie Choset

SLAM

  1. SLAM by Prof. Cyrill Stachniss Univ. of Freiburg

Probability

  1. Probability by Joe Blitstein, Harvard
  1. Probabilistic Systems Analysis and Applied Probability by Prof. John Tsitsiklis & Prof. Patrick Jaillet, MIT

Miscellaneous

  1. Nonlinear Dynamics by Prof. Steven Strogatz, Conrell
  1. Koopman Analysis by Prof. Steve Brunton, UW
  1. Motion Control by Rajesh Rajamani, Univ. of Minnesota
  1. Fundamentals of Computing Specialization by Rice University
  1. C++ by Udacity
  1. Data Structures and Algorithms by Udacity
  1. C++ Design Patterns by Dmitri Nesteruk
  1. Intermediate Software Design by Prof. Douglas C. Schmidt, Vanderbilt Univeristy
  1. Angrave's crowd-sourced System Programming wiki-book! This wiki was actively built and maintained 2014-2018 by students and faculty from the University of Illinois
  1. Embedding Sensors and Motors Specialization by Prof. James Zweighaft and Prof. Jay Mendelson, UCB
  1. Real Analysis by DTU
  1. Real-time Operating Systems

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