| Lecturer |
Berthold Bäuml |
| Module |
IN2349 |
| Type |
Lecture |
| Semester |
SS 2017 |
| ECTS |
2.0 |
| SWS |
2V |
| Audience |
Elective course in Robotics, Cognition, Intelligence (Master's Program) |
| Time & Place |
Do 12 - 14 MI HS 2 |
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Description
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).
- Introduction/Motivation slides
- Machine Learning Basics I: slides
- linear algebra, probability & information theory, continuous optimization
- Machine Learning Basics II: slides
- Revisiting Bayesian probabilistic inference,
bias-variance decompostion,
classifictation & logistic regression
- (Deep) Neural Networks I: slides
- XOR problem; universal function approximator; backpropagation; TensorFlow
- Deep Neural Networks II: slides
- mutlilayer MNIST, TensorFlow/TensorBoard, activation functions, weight initialization, regularization
- Deep Neural Networks III: slides
- adversarial training; convolutional neural networks, examples: AlexNet, ...
- Deep Learning IV and Application in Robotics: slides
- CNN applications; transfer learning; gradient descent revisited; momentum methods
- Deep Learning V and Application in Robotics: slides
- cross-validation; hyperparameter search; applications in robotics: grasping
Course Materials
Literature
- Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
- Kevin Murphy. “Machine Learning: A Probabilistic Perspective”, MIT Press 2012
Example Programs
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