Machine Learning for Robotic Systems 1


This lecture provides an overview of essential and current methods  and concepts of Machine Learning for different robotic applications. It covers also their underlying mathematical and statistical methods. Important fundamental terminology, concepts and methods are presented for various topics including:

  • Model selection, machine learning bias vs. parameter optimization
  • Training, test, validation, generalization, overfitting, regularization
  • Supervised vs unsupervised learning
  • Regression
  • Classifications
  • Neural Networks
  • Gaussian mixtures, Gaussian mixture regression

And other interesting topics 

Language of instructionEnglish