Learning various concepts, methods, and key technologies relevant in the supervised and unsupervised machine learning areas. This knowledge is general purpose and students will practically apply it to a set of very relevant photonic applications, namely electromagnetic and optical designs, and data analysis for biophotonics. Optics will be presented as part of electromagnetics, showing connections between full-wave, diffractive and refractive (geometrical optics) solutions. Design can include data preparation, optimization and tolerance analysis as practical activities. Biophotonic data will be used for classification problems and the importance of pre-processing techniques will be shown (e.g., for Raman spectroscopy data). Electromagnetics and biophotonics connections will be shown for example when using nanostructures for Surface Enhanced Raman spectroscopy. Students will acquire insight, skills and experience in solving photonic/optical design and data analysis problems using machine learning techniques.