The
lecture covers the foundations of pattern recognition from a
probabilistic point of view. The following topics are discussed:
- Basics (e.g., stochastics, model selection, Curse of Dimensionality, decision and information theory)
- Distributions (e.g., multinomial, Dirichlet, Gaussian and Student distributions, nonparametric estimation)
- Linear models for regression
- Linear models for classification
- Neural networks
- Kernel methods
| Start of the lecture: | TBA | | |
|---|---|---|---|
| Start of the exercise: | TBA |
In this lab, students learn how to work with humanoid robots. The lab consists of the following content:
- Introduction to working with humanoid robots
- Introduction to the Robot Operating System (ROS)
- Application of algorithms from computer vision
- Getting to know and handling 3D sensors
- Introduction and application of basic machine learning algorithms
- Basics of cooperation between human-robot / robot-robot
- Enhancement of robot sensor technology for higher interaction capabilities.
The lab concludes with a group project.
The kickoff event is tentatively scheduled for October 23, 2023, at 2:00 pm.