Courses, tutorials, lectures

• Probabilistic Machine Learning (28 lectures, ~1h30m):
  • course on the probabilistic (“Bayesian”) paradigm for machine learning
• Robotics from UPenn (167 lectures; 1-15 minutes):
  • series on general concepts of robotics from University of Pennsylvania
• Missing Semester (11 lectures; ~1h):
  • series on what they did not teach you in the lectures (shell, vim, debugging, profiling, git, and others)
• Robotic Systems (draft):
  • work in progess on book on robotic systems from Kris Hauser from University of Illinois at Urbana-Champaing
• KSP MFF CUNI: Programatorske kucharky (CZ):
  • 18 well-written manuals on introduction to algorithmization
  • available also in single pdf file
• Machine Learning cheat sheet (90 pages):
  • work in progress