Topics of the School

Covered areas include (but are not limited to):

• Design and development of biophotonic devices;
• Biophotonic imaging;
• Complexity in biophotonic systems;
• Complexity in new materials, devices and emerging components such as micro-resonators and plasmonic nanostructures;
• Artificial Intelligence (AI):
  • Statistics for Machine Learning;
  • Introduction to programming languages in the field of AI (Python, R) of interest in Biophotonics, and the best known libraries (scikit learn, Keras, Tensorflow, etc.);
  • machine learning;
  • Deep learning:
    • Convolutional Neural Networks (CNN);
    • Neural networks for temporal signals (RNN, LSTM, Transformers);
    • Auto encoders;
    • GAN;
    • Graph Neural Networks;
    • Reinforcement Learning;
    • Transfer Learning, Data augmentation, etc.;
• Applications of AI for the design of biophotonic sensors;
• Applications of AI for the analysis of biophotonic data;
• Introduction to Explainability, Interpretability, Trustworthy.