Igor Gilitschenski: Catalogue data in Spring Semester 2018

NameMr Igor Gilitschenski
Autonome Systeme
ETH Zürich, LEE J 317
Leonhardstrasse 21
8092 Zürich
DepartmentMechanical and Process Engineering

151-0634-00LPerception and Learning for Robotics
Number of participants limited to: 30

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.
4 credits1AC. D. Cadena Lerma, I. Gilitschenski, R. Siegwart
AbstractThis 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.
ObjectiveApplying Machine Learning methods for solving real-world robotics problems.
ContentDeep Learning for Perception; (Deep) Reinforcement Learning; Graph-Based Simultaneous Localization and Mapping
Lecture notesSlides will be made available to the students.
LiteratureWill be announced in the first lecture.
Prerequisites / NoticeThe 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. Furtheremore, good knowledge of programming in C++ and Python is required.