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.
Lehrveranstaltungen
Nummer
Titel
Umfang
Dozierende
151-0634-00 A
Perception and Learning for Robotics
The lectures take place during the first two weeks of the Semester. The venue will be announced later.
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.
Lernziel
Applying Machine Learning methods for solving real-world robotics problems.
Inhalt
Deep Learning for Perception; (Deep) Reinforcement Learning; Graph-Based Simultaneous Localization and Mapping
Skript
Slides will be made available to the students.
Literatur
Will be announced in the first lecture.
Voraussetzungen / Besonderes
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.
Leistungskontrolle
Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Repetition nur nach erneuter Belegung der Lerneinheit möglich.
Zusatzinformation zum Prüfungsmodus
The grade is based on the realization of a project, presentation and demo (50%), the project report (40%) and quizzes/exercises during the lecture block (10%).
Lernmaterialien
Keine öffentlichen Lernmaterialien verfügbar.
Es werden nur die öffentlichen Lernmaterialien aufgeführt.
Leistungskontrolle als Semesterkurs