When you collect sensory data, is possible that you face very different kind of data. For example, you could have gyroscope data but also some Twitter posts. Is not easy to combine this information, but it is possible to extract some useful features from the dataset in order to maximize the predictive performance of the final model. Different options are available for this, and we will consider features that use the notion of time, both in the time domain and the frequency domain, and features for unstructured data.
