Machine Learning

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.

Sensory data are very common nowadays. Every technological device you possess it is full of sensors that you or some app can use in order to get insights about your behavior. Normally, this kind of data is called time series data because it is recorded every tot seconds. The app that you are using or you if you know how to extract this type of data, might incur in some problems give how the data is recorded.

The idea of coevolution applied to Computational Intelligence (the branch of Artificial Intelligence assigned to theory, design, application and development of biologically and linguistically motivated computational paradigms).

PyData Amsterdam 2019 was hosted last week by Booking.com in Amsterdam. The two days conference (plus one with workshops) allowed experts and users of data analytics tools to share their research, approaches, and mistakes. During the conference, one challenge was posted by GoDataDriven with a Lego Set as a prize. In this post, I present my approach to the problem which arrived fifth in the final classific (No Lego for me) but it reached that point with fewer requests compared to the other methods.

Modelling human behaviour is still an ongoing challenge that spaces between several fields like social science, artificial …