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| Detailed information |
| Original study plan |
Master's programme Artificial Intelligence 2019W |
| Objectives |
This course introduces how static and interactive visualization can be facilitated to analyze and better understand AI processes and black-box algorithms during all three phases: model building, model training, and model usage.
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| Subject |
- Visualization Techniques and Tools for AI
- Visualization Support in Deep Learning
- Supporting Interpretability & Explainability through Visualization
- Debugging & Improving Models Using Visualization
- Comparing & Selecting Models Using Visualization
- Visualizing Network Architectures, Learned Model Parameters (Edge Weights, Convolutional Filters), Computational Units (Activations, Gradients for Error Measurement), Neurons, Aggregated Information
- Case Studies and Selected Research
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| Criteria for evaluation |
Written exam (oral exam in exceptional cases) combined with practical exercises.
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| Methods |
Slide presentation with case studies, tutorials, in-class exercises, and practical project activities.
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| Language |
English |
| Changing subject? |
No |
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