Machine Learning in Robotics (Lecture w/ Exercise)
The lecture imparts understanding of methods from pattern classification, recognition and machine learning. In particular this lecture leads the students to the robotic applications using machine learning techniques. The following topics are included: Applications of Machine Learning for Robots, Probability and Statistics, Density Estimation, linear regression, Pattern Classifiers, Probabilistic Methods for Classification, Dimensionality Reduction, PCA, Feature Selection, Statistical Clustering, Unsupervised Learning, EM algorithm, Validation, Support Vector Machines, Markov process, Hidden Markov Models, Dynamic Time Warping, Gaussian Mixture Model,
Previous knowledge expected
Fundamentals of Linear Algebra, Probability and Statistics
Assessment (exam method and evaluation)
written, 90 min
Lecture work sheets
R. O. Duda, P. E. Hart and D. G. Stork, 2001, Pattern Classification, 2nd ed., Wiley.