In this chapter, we introduced the Dynamic Time Warping (DTW) algorithm, which is an excellent tool for finding similarity between vectors with an unsupervised learning model. We presented an implementation of DTW to find similarity between a set of images, which works very well in most cases. This method can be used in several other problems, in a variety of areas, such as robotics, computer vision, speech recognition, and time series analysis. We also saw how to turn an image into a time series with the PIL library. Finally, we learned how to implement DTW with the Python mlpy library.

In the next chapter, we will present how simulation can help us in data analysis and how to model pseudo-random events.