In this chapter, we looked into detecting anomalous and suspicious patterns. We discussed the two fundamental approaches focusing on library encoding either positive or negative patterns. Next, we got our hands on two real-life datasets, where we discussed how to deal with unbalanced class distribution and perform anomaly detection in time series data.

In the next chapter, we'll dive deeper into patterns and more advanced approaches to build pattern-based classifier, discussing how to automatically assign labels to images with deep learning.