This lecture provides an overview of essential and current methods and concepts of Machine Learning for different robotic applications. It also covers 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
• Probabilistic Modeling
• Classifications
• Neural Networks
• Gaussian mixtures, Gaussian mixture regression
• Deep Learning with Convolutional Neural Networks

And other interesting topics