CTU FEE Moodle
Robot Learning
B232 - Summer 23/24
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Robot Learning - B3B33VIR
| Credits | 4 |
| Semesters | Winter |
| Completion | Assessment + Examination |
| Language of teaching | Czech |
| Extent of teaching | 2P+2L |
Annotation
The course teaches application of machine learning methods and optimization on well-known robotic problems, such as semantic segmenation from RGB-D data or reactive motion control. The core of the course represents teaching of deep learning methods.
Stidents will use basic knowledge from optimization and linear algebra such as robut solving of overdetermined systems of (non)linear (non)homogenous equations or gradient minimization methods. The labs are divided into two parts, in the first one, the students will solve basic tasks in PyTorch, in the second one, individual semestral work.
Stidents will use basic knowledge from optimization and linear algebra such as robut solving of overdetermined systems of (non)linear (non)homogenous equations or gradient minimization methods. The labs are divided into two parts, in the first one, the students will solve basic tasks in PyTorch, in the second one, individual semestral work.
Study targets
The course teaches application of machine learning methods and optimization on well-known robotic problems, such as semantic segmenation from camera and deep images or reactive robot control. The core of the course represents teaching of deep CNN application methods.
Course outlines
1. Overview and lecture outline.
2. Regression ML/MAP
3. Classification ML/MAP
4. Neural networks, backpropagation
5. Convolution leyer, backpropagation
6. Normalization leyer (BatchNorm, InstanceNorm, ...) a backpropagation
7. Training I (SGD, momentum and their convergence ratio)
8. Training II (Nester gradient, Adam optimizer, activation function impact on optimization problems)
9. Architectures of deep neural networks I: detection (yolo), segmentation (DeepLab), classification (ResNet)
10. Architectures of deep neural networks II: pose regression, spatial transformer nets.
11. Generative Adversarial Networks, Cascaded Refinement Networks, Style Transfer Networks
12. Reinforcement learning in robotics (policy gradient, imitation learning, actor-critic, aplications)
13. Learning from weak annotations (weak-supervision, self-supervision)
14. Presentation of semestral works
2. Regression ML/MAP
3. Classification ML/MAP
4. Neural networks, backpropagation
5. Convolution leyer, backpropagation
6. Normalization leyer (BatchNorm, InstanceNorm, ...) a backpropagation
7. Training I (SGD, momentum and their convergence ratio)
8. Training II (Nester gradient, Adam optimizer, activation function impact on optimization problems)
9. Architectures of deep neural networks I: detection (yolo), segmentation (DeepLab), classification (ResNet)
10. Architectures of deep neural networks II: pose regression, spatial transformer nets.
11. Generative Adversarial Networks, Cascaded Refinement Networks, Style Transfer Networks
12. Reinforcement learning in robotics (policy gradient, imitation learning, actor-critic, aplications)
13. Learning from weak annotations (weak-supervision, self-supervision)
14. Presentation of semestral works
Exercises outlines
In the first half of labs, the students will solve basic tasks in PyTorch, in the second one, the students will work on individual semestral works.
Literature
Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep learning, MIT press, 2016 http://www.deeplearningbook.org
Requirements
No data.