Credits

Course Development

Module Lead

  • CIVE70111 Machine Learning Module Team
  • Department of Civil & Environmental Engineering
  • Imperial College London

Interactive Demos Development

Design & Implementation

  • Interactive visualization framework
  • Machine learning algorithm implementations
  • Educational interface design

Technical Stack

  • Pure JavaScript implementations for browser compatibility
  • HTML5 Canvas for high-DPI visualizations
  • PyTorch for model pretraining (CNN & Multi-Armed Bandit demos)
  • MathJax for mathematical notation rendering

Data Sources

  • MNIST dataset for CNN demonstrations
  • Civil engineering datasets for gradient descent examples
  • Synthetic data generation for classification and regression demos

Open Source Libraries

  • MathJax - Mathematical notation rendering
  • PyTorch - Deep learning framework (training only)
  • NumPy - Numerical computing (training only)

Acknowledgments

These interactive demonstrations were developed to support advanced undergraduate and MSc students in the CIVE70111 Machine Learning module. The tools are designed to provide hands-on experience with fundamental machine learning concepts and algorithms.

Special consideration has been given to tablet compatibility and static serving requirements to ensure accessibility across different learning environments.

License & Usage

These materials are developed for educational purposes as part of the CIVE70111 module at Imperial College London.