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” |
Content
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
Literature
Lecture work sheets
R. O. Duda, P. E. Hart and D. G. Stork, 2001, Pattern Classification, 2nd ed., Wiley.