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Laboratory: Deep Learning Lab

Welcome to the Deep Learning Lab a joint teaching effort of the Robotics (R), Robot Learning (RL), Computer Vision (CV), and Machine Learning (ML) Labs. Deep learning has brought a revolution to AI research. A good understanding of the principles of deep networks and experience in training them has become one of the main assets for successful research and development of new technology in machine learning, computer vision, and robotics. In this course, we will teach students the practical knowledge that is needed to do research with deep learning, imitation learning, and reinforcement learning. This course consists of a mixture of lectures, exercises and group projects. The course is divided into four tracks that focus on different aspects of deep learning research. Please register for only one of the tracks mentioned below:

DDL

Track 1: Robotics (11LE13P-7302)
Track 2: Robot Learning (11LE13P-7321)
Track 3: Computer Vision (11LE13P-7305)
Track 4: Automated Machine Learning (11LE13P-7312) [Frank is on parental leave so please contact the organizers]

Please fill in this form with your information if you 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
Course overview via Zoom, Meeting ID: 939 4672 4859, Password: DLlab2020

Requirements: Fundamental programming skills in Python. Basic knowledge of deep learning, equivalent with having passed the Fundamentals of Deep Learning course. Some experience with the Linux toolchain (text editor, compiler, linker, debugger) is recommended.

Lectures, Assignments & Forum: ILIAS course

Remarks: Due to the Corona crisis, the entire Deep Learning Lab will be held online. This includes the lectures, exercises and group projects. Video lectures and exercises will be uploaded to ILIAS on the day of the lecture. Please watch the lecture and start working on the exercises. You may post questions on the lecture by inserting comments in the video page or post questions about the exercises in the forum. We will then have a Zoom meeting in the week following the lecture where all the questions will be discussed.

Schedule

    Phase I: Lectures
  • 13.05.2020: Course Overview
           Lecture 1: Deep Learning for Computer Vision
           Hand out Exercise 1
  • 20.05.2020: Virtual meeting solving open questions
  • 27.05.2020: Lecture 2: Automated Machine Learning
           Exercise 1 submission due
           Hand out Exercise 2
  • 03.06.2020: Virtual meeting solving open questions
  • 10.06.2020: Lecture 3: Deep Imitation and Reinforcement Learning
           Exercise 2 submission due
           Hand out Exercise 3
           Presentation of topics for final project

  • Phase II: Project
  • 17.06.2020: Virtual meeting solving open questions
           Final project selection due
  • 24.06.2020: Project progress discussion
           Exercise 3 submission due
  • 01.07.2020: Project milestone 1
  • 08.07.2020: Project progress discussion
  • 15.07.2020: Project milestone 2
  • 22.07.2020: Project progress discussion
  • 29.07.2019: Project milestone 3
  • 03.08.2020: Submission Final Project (Code + Poster/Presentation)
  • TBD.08.2020: Project presentations

Material

Sponsor

Support for this course was generously provided by the Google Cloud Platform Education Grant.

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