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Course content
- Introduction to A1
- Quality Characteristics for A1-Based Systems
- Machine Learning (ML) — Overview
- ML – Data
- ML Functional Performance Metrics
- ML — Neural Networks and Testing
- Testing A1-Based Systems Overview
- Testing A1-Specific Quality Characteristics
- Methods and Techniques for the Testing of A1-Based Systems
- Test Environments for A1-Based Systems
- Using A1 for Testing
Course content
- Octave Neural Network for Beginners
- Octave Neural Network – Advanced
Course content
- Introduction
- 3D reconstruction
- 3D reconstruction : modules
- NeRF : Neural Radiance Fields
- Novel view synthesis
- Mesh extraction
- Going further: scientific papers
- Tools and open source implementations
- Conclusion
Course content
- Introduction
- DCGAN and WGAN
- cGAN – Pix2Pix and CycleGAN
- SRGAN and ESRGAN
- StyleGAN
- VQGAN + CLIP – text to image
- Other types of GANs
- Additional content 1: Artificial neural networks
- Additional content 2: Convolution neural networks
- Final remarks
Course content
- Introduction
- Statistics Projects
- Bayesian Projects
- Machine Learning
- Deep Learning Projects
- Machine Learning A1
Course content
- Introduction to Backpropagation Algorithm
- Backpropagation Algorithmn- Matlab Application
Course content
- Big Data Big Picture
- Big Data Overview
- DB-SQL
- Big Data Core
- Artificial Intelligence
- Machine Learning
- Deep Learning
- Presentation Tips For A1 and Big Data Environment
- Final Revision
Course content
- Background Theory
- NEAT Theory
- NEAT Application
Course content
- Introduction to XAI
- Demonstration of By Design Interpretable Models: Glass
- Box
- LIME (Local Interpretable Model Agnostic Explanations)
- SHAP (SHapIey Additive exPIanations)
- Counterfactual Explanations
- Google’s What-if Tool (WIT) for A1 fairness and Counterfactuals
- Layer-wise Relevance Propagation (LRP)
- Contrastive Explanations Method (CEM)
- Useful Resources for XAI
- Final Quiz
- Surprise on Completion of Course
- Other resources from the Instructor
- Acknowledgement
Course content
- Introduction
- Project Setup
- Data Preparation
- Data Preprocessing and Cleaning
- Train Named Entity Recognition (NER) model
- Predictions
- Improve Model Performance
- Document Scanner
- Document Scanner Web App
- Appendix
- BONUS
