Advanced Deep Learning for Robotics (IN2349)
Course 0000003810 in SS 2023
General Data
| Course Type | lecture with integrated exercises |
|---|---|
| Semester Weekly Hours | 4 SWS |
| Organisational Unit | Informatics 6 - Chair of Robotics, Artificial Intelligence and Real-time Systems (Prof. Knoll) |
| Lecturers |
Berthold Bäuml Responsible/Coordination: Darius Burschka |
| Dates |
Thu, 12:00–14:00, MI HS2 |
Assignment to Modules
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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 | This course is 2V + 2P, the practical part is a semester long project. The course will be given by B. Bäuml. Details about the course will be given on the course webpage: https://bbaeuml.github.io/tum-adlr/ This is the advanced deep learning lecture with a specific focus on Robotics and deep reinforcement learning (including a guest lecture from DeepMind). For the semester long project in the practical part, for each student $1000 credits in the Google Cloud are provided via a Google Educational Grant. Taking the “Introduction to Deep Learning” course is expected. 1. Introduction & Recap of Deep Learning 2. Advanced Network Architectures & Recursive Neural Networks (LSTMs) 3. Hyperparameter & Architecture Search Bayesian optimization, network architecture search (NAS) 4. Adversarial Samples & Adversarial Training 5. Bayesian Deep Learning Bayesian learning, variational inference, Monte Carlo dropout method 6. Generative Models: VAEs & GANs variational auto-encoders, generative neural networks (WGAN-GP) 7. Data Efficient Learning: Transfer & Semi-Supervised Learning 8. Deep Reinforcement Learning I MDPs, Q-iteration, Bellman equation, deep Q-learning, example: Atari-games 9. Deep Reinforcement Learning II policy gradients, REINFORCE, actor-critic algorithm, TRPO, PPO, robotic applications 10. Deep Reinforcement Learning III advanced methods: DDPG, soft Q-learning, soft actor-critic (SAC), robotic applications 11. Deep Reinforcement Learning IV model-based DRL, MCTS + learned heuristics, AlphaZero, model learning, PDDM, robotics applications 12. Guest Lecture from DeepMind Recent Developments in Deep Reinforcement Learning for Robotics |
|---|---|
| Links |
Course documents E-Learning course (e. g. Moodle) Additional information TUMonline entry |
Equivalent Courses (e. g. in other semesters)
| Semester | Title | Lecturers | Dates |
|---|---|---|---|
| WS 2023/4 | Advanced Deep Learning for Robotics (IN2349) | Bäuml, B. | |
| 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. |
|
| SS 2017 | Deep Learning in Robotics (IN2349) |
Bäuml, B.
Responsible/Coordination: Burschka, D. |