Credits

About the Project

These interactive machine learning demonstrations were developed to support undergraduate and MSc students in the CIVE70111 Machine Learning module at Imperial College London. The tools provide hands-on experience with fundamental machine learning concepts and algorithms, bridging the gap between theoretical understanding and practical application within civil engineering contexts.

Each demonstration features real-time visualization, interactive parameter controls, and educational challenges designed to enhance student understanding of increasingly complex ML concepts.

Development Team

Centre for Transport Engineering & Modeling, Department of Civil & Environmental Engineering, Imperial College London

Data Science in Civil & Environmental Engineering

These demonstrations are integral to the Data Science Stream, a postgraduate educational pathway offered by the Department of Civil & Environmental Engineering:

  • MSc Transport
  • MSc Environmental Engineering
  • MSc Geotechnical Engineering
  • MSc General Structural Engineering

The Data Science Stream equips engineers with advanced computational skills and modern ML toolkits, preparing them with practical skills required for our sector's adoption of AI-driven modeling and data-driven solutions. Students develop comprehensive expertise across multiple technical domains:

  • Statistical Modeling - Advanced theory with R programming implementation
  • Machine Learning - Supervised, unsupervised, and reinforcement learning using Python
  • Data Engineering - Database development, cloud platforms, and advanced visualization techniques
  • Applied Research Projects - Practical applications aligned with specific civil engineering specializations

Learn more about the Data Science Stream →

License & Usage

These materials are developed for educational purposes at Imperial College London.