Deep Learning in Robotics (IN2349)
Course 0000003810 in SS 2017
General Data
| Course Type | lecture |
|---|---|
| Semester Weekly Hours | 2 SWS |
| Organisational Unit | Informatics 6 - Chair of Robotics, Artificial Intelligence and Real-time Systems (Prof. Knoll) |
| Lecturers |
Berthold Bäuml Responsible/Coordination: Darius Burschka |
| Dates |
Assignment to Modules
-
IN2349: Weiterführendes Deep Learning für die Robotik / Advanced Deep Learning for Robotics
This module is included in the following catalogs:- Focus Area Bio-Sensors in M.Sc. Biomedical Engineering and Medical Physics
Further Information
Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.
| additional remarks | Notice:The lecture is currently only approved for the master's course "Robotics, Cognition, Intelligence” and you might have to check with the student office, if this course will be recognized. The lecture covers the mathematical foundations and the efficient implementation of modern Deep Learning Neural Network Architectures (incl. One-Shot Learning) and its application to problems with real robots (e.g., tactile material classification with a robotic hand). Key points: Overview: Shallow Neural Networks (Perceptrons, Backpropagation, Automatic Differentiation, Autoencoder Networks, Constrained Optimization) Deep Neural Networks (Hierarchical Feature Extraction; Stochastic Optimization, Regularization Methods, Convolutional Neural Networks) Software Frameworks (Google TensorFlow, Facebook Torch, Theano, Caffee, Microsoft CNTK) Applications (“Tactile Material Classification”, “Hand-Eye Coordination for Robotic Grasping”) Robustness (Validation Methods, Networks with Confidence Prediction) Recursive Neural Networks Transfer & One Shot Learning (Siamese Neural Networks, Neural Turing Machines) Excursion to Institute of Robotics and Mechatronics |
|---|---|
| Links | TUMonline entry |
Equivalent Courses (e. g. in other semesters)
| Semester | Title | Lecturers | Dates |
|---|---|---|---|
| WS 2023/4 | Advanced Deep Learning for Robotics (IN2349) | Bäuml, B. | |
| SS 2023 | Advanced Deep Learning for Robotics (IN2349) |
Bäuml, B.
Responsible/Coordination: Burschka, D. |
Thu, 12:00–14:00, MI HS2 |
| WS 2022/3 | Advanced Deep Learning for Robotics (IN2349) | Bäuml, B. | |
| SS 2022 | Advanced Deep Learning for Robotics (IN2349) |
Bäuml, B.
Responsible/Coordination: Burschka, D. |
Thu, 12:00–14:00, MI HS2 |
| WS 2021/2 | Advanced Deep Learning for Robotics (IN2349) | Bäuml, B. |
Thu, 12:00–15:30, virtuell Thu, 12:00–15:30, virtuell |
| SS 2021 | Advanced Deep Learning for Robotics (IN2349) | Bäuml, B. Burschka, D. | |
| WS 2020/1 | Advanced Deep Learning for Robotics (IN2349) | Bäuml, B. | |
| SS 2020 | Advanced Deep Learning for Robotics (IN2349) | Bäuml, B. | |
| SS 2019 | Advanced Deep Learning for Robotics (IN2349) | Bäuml, B. Burschka, D. | |
| SS 2018 | Advanced Deep Learning for Robotics (IN2349) |
Bäuml, B.
Responsible/Coordination: Burschka, D. |