Biosignals are rich, messy, and highly variable. In this tutorial, we offer a practical entry point into AI/ML for biosensing by starting from what makes signals like electroencephalography (EEG) and photoplethysmography (PPG) different from typical datasets: their frequency–magnitude structure, noise and artifacts, and strong variability across people and hardware (often experienced as domain shift). We show how this domain knowledge should shape the way you represent signals and choose models.

We then use statistical analysis as the bridge from raw waveforms to meaningful features, connecting intuitive signal properties to time-domain statistics and frequency-domain summaries that often carry the most useful information. From there, we explain how classical machine learning uses these engineered features, how deep learning can learn representations directly from raw inputs, and how hybrid pipelines can combine both worlds. We also discuss practical, lightweight approaches to interpretability and explainability that help researchers and practitioners trust and debug biosignal models.

Finally, we focus on multi-task learning as an extension for many biosensing problems. We cover how to define tasks that help rather than fight each other, why multi-loss training can become unstable, and how adaptive loss weighting can reduce manual tuning and keep training balanced. Participants will leave with concrete pitfalls to avoid and practical takeaways they can apply immediately.