- Info
Masterpraktikum, Deep Learning Lab
Prof. Wolfram Burgard,
Prof. Thomas Brox,
Prof. Frank Hutter,
Asst. Prof. Joschka Boedecker,
Andreas Eitel,
Abhinav Valada,
Maria Hügle
Aaron Klein
Matilde Gargiani
Gabriel Oliveira
Tonmoy Saikia
Welcome to the Deep Learning Lab a joint teaching effort of the Robotics (R), Computer Vision (CV), Machine Learning (ML) and Neurorobotics (NR) 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 want to teach students the practical knowledge that is needed to do research with deep learning and reinforcement learning.
The course is divided into two main tracks that focus on different aspects of deep learning research.
Track 1: Robotics and Reinforcement Learning (R/NR). 11LE13P-7302 and 11LE13P-7320.
Track 2: Computer Vision and Automated Machine Learning (CV/ML). 11LE13P-7305, 11LE13P-530-15 and 11LE13P-7312.
Lecture/Exercises:
|
Tuesday, 14.00-16.00 Room: Kinohörsaal/computer pools, Building 082
|
Beginning: |
Tuesday, October 16, 2018
|
Communications: |
Join our Slack channel for updates on the course (dl-lab-freiburg.slack.com) You can join with this invitation link.
|
Requirements: |
Fundamental programming skills in Python. Basic knowledge about machine learning techniques
(supervised learning, classification, regression), e.g. passed the Deep Learning lecture.
Some experience with the Linux toolchain (text editor, compiler, linker, debugger) is recommended.
|
Schedule: |
Phase I: Neural Networks Recap
16.10.2018: Lecture 1: recap neural network basics, hand out Exercise 1
23.10.2018: Meeting solving open questions, final date to switch Tracks (done via email, or best directly via HISinOne)
30.10.2018: Lecture 2: convolutional neural networks, hand out Exercise 2, submission Exercise 1
06.11.2018: Meeting solving open questions
Phase II: Split up in 2 Tracks (R/NR) and (CV/ML)
13.11.2018: Lecture 3: Track1: Imitation Learning, Track2: Semantic Segmentation, hand out Exercise 3, submission Exercise 2
20.11.2018: Meeting solving open questions
27.11.2018: Meeting solving open questions
04.12.2018: Lecture 4: Track1: Reinforcement Learning, Track2: Automated ML, hand out Exercise 4, submission Exercise 3
11.12.2018: Meeting solving open questions
18.12.2018: Presentation topics for Final Project
Phase III: Final Project
08.01.2018: Start project work, submission Exercise 4
15.01.2018: Meeting solving open questions
22.01.2018: Meeting solving open questions
29.01.2018: Meeting solving open questions
03.02.2018: submission Final Project (Poster + Code)
05.02.2018: Final project poster session in Kinohörsaal from 14.00-16.00
|
|
Support for this course was generously provided by the Google Cloud Platform Education Grant.
|
|