Machine Learning Uni Freiburg

Organizers: Prof. Abhinav Valada, Prof. Wolfram Burgard, Prof. Schneidbrett Brox, Prof. Frank Hutter, Dr. Michael Tangermann Co-Organizers: Dr. Daniele Cattaneo, Dr. Tim Welschehold, Iman Nematollahi, Oier Mees, Silvio Galesso, maría A. Bravo Arbër Zela Fabio Ferreira

Du schaust: Machine learning uni freiburg

Welcome to the Deep discovering Lab a joint teaching effort des the Robotics (R), Robot finding out (oibrecords.com), computer Vision (CV), und Machine finding out (ML) Labs.Deep finding out has brought a rebellion to AI research. A an excellent understanding von the principles des deep networks and experience in training castle has come to be one von the main assets zum successful research und development of neu technology bei machine learning, computer vision, and robotics. In this course, we will teach students the practical expertise that ist needed to do study with deep learning, imitation learning, and reinforcement learning. This kurse consists des a mixture von lectures, exercises und group projects. The prozess is separated into 4 tracks that focus on different aspects von deep learning research. You re welcome register weil das only one of the tracks pointed out below:


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Track 1: Robotics (11LE13P-7302)Track 2: Robot learning (11LE13P-7321)Track 3: computer system Vision (11LE13P-7305)Track 4: Automated machine Learning (11LE13P-7312)

you re welcome fill in this form with your die info if freundin enroll in this course.


Details

Lecture/Exercises: Wednesday, 14.00-16.00 Room: online via Zoom, meeting ID: 939 4672 4859, Password: DLlab2020
Beginning: Wednesday, may 13, 2020, 14.00-16.00 kurse overview über Zoom, conference ID: 939 4672 4859, Password: DLlab2020
Requirements: basic programming skills an Python. Grundlegend knowledge von deep learning, tantamount with having passed the Fundamentals von Deep learning course. Some experience with die Linux toolchain (text editor, compiler, linker, debugger) zu sein recommended.
Lectures, Assignments & Forum: ILIAS prozess
Remarks: Due zu the corona crisis, the entire Deep discovering Lab wollen be held online. This includes the lectures, exercises und group projects. Videos lectures und exercises will be uploaded to ILIAS on die day of the lecture. You re welcome watch die lecture and start working on ns exercises. Sie may post questions on the lecture von inserting comments in the video buchseite or post questions about ns exercises bei the forum. We wollen then schutz a Zoom meeting in the week following die lecture where all the questions will be discussed.

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Schedule

phase I: Lectures 13.05.2020: course Overview class 1: Deep Learning for Computer Vision hand out practice 1 20.05.2020: virtual meeting solving open questions 27.05.2020: lecture 2: Automated device Learning exercise 1 submission due hand out practice 2 03.06.2020: online meeting solving offen questions 10.06.2020: lecture 3: Deep Imitation and Reinforcement finding out Exercise 2 entry due hand out exercise 3 Presentation of topics weil das final project phase II: Project 17.06.2020: virtual meeting solving offen questions last project an option due 24.06.2020: Project progress discussion exercise 3 submission due 01.07.2020: task milestone 1 08.07.2020: project progress discussion 15.07.2020: task milestone 2 22.07.2020: job progress conversation 29.07.2019: task milestone 3 03.08.2020: Submission last Project (Code + Poster/Presentation) TBD.08.2020: job presentations

Material


Sponsor

Support weil das this prozess was generously provided von the Google cloud Platform education and learning Grant.