To apply for the course please create a CV in pdf of max. 2 pages, including your machine learning and/or robotics experience. Please send the pdf to cesarc@ethz.ch for approval.
Abstract
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.
Learning objective
Applying Machine Learning methods for solving real-world robotics problems.
Content
Deep Learning for Perception; (Deep) Reinforcement Learning; Graph-Based Simultaneous Localization and Mapping
Lecture notes
Slides will be made available to the students.
Literature
Will be announced in the first lecture.
Prerequisites / Notice
The students are expected to be familiar with material of the "Recursive Estimation" and the "Introduction to Machine Learning" lectures. Particularly understanding of basic machine learning concepts, stochastic gradient descent for neural networks, reinforcement learning basics, and knowledge of Bayesian Filtering are required. Furtheremore, good knowledge of programming in C++ and Python is required.
