Perception and Learning for Robotics

Short Description

This course covers tools from statistics and machine learning enabling the participants to deploy these algorithms as building blocks for perception pipelines on robotic tasks. All mathematical methods provided within the course will be discussed in context of and motivated by example applications mostly from robotics. The main focus of this course are student projects on robotics, with an emphasis on robot perception.

Requirements

The students are expected to be familiar with material of the "Recursive Estimation" and the "Learning and Intelligent Systems" lectures. Particularly understanding of basic machine learning concepts, stochastic gradient descent for neural networks, reinforcement learning basics, and knowledge of Bayesian Filtering are required. Furthermore, good knowledge of programming in C++ and Python is required.

Lecture Dates and Topics

Feb. 26 and 28, and Mar. 02 (14:00 to 18:00)

All material (such as slides and information on projects) will be made available through Moodle.