Machine Learning in Robotics (Lecture w/ Exercise)

Lecturer Lee Dongheui
Allocation to curriculum See TUMonline
Offered in Sommersemester 2019
Semester weekly hours 3  
Scheduled dates See TUMonline
Registration See “Course criteria & registration”



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.

Online information