Summary

Let’s recap what we have learned in this chapter. We began our discussion with a brief introduction to the history of AI. You learned how it has evolved into ML and then DL. Then, we looked at different ways of defining the ML taxonomy, which gave you a systematic way of describing ML methods. In the taxonomy, you learned various types of ML algorithms and their applications, for instance, supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. More details about those algorithms will be provided in the following chapters. In addition, you also learned about various data types that are common in ML and the definition of model modality. Finally, we shifted our focus to the recent advances in DL and reiterated its limitations and the security and safety concerns for AI.

The next chapter will further explore ML methods, including where they are applicable. You will extend your knowledge of this chapter and learn more about the...