Introduction

This chapter starts by describing the organization of the book, which consists of three parts. Part I discusses some basic concepts, including, for instance, what metalearning is and how it is related to automatic machine learning (AutoML). This continues with a presentation of the basic architecture of metalearning/AutoML systems, discussion of systems that exploit algorithm selection using prior metadata, methodology used in their evaluation, and different types of meta-level models, while mentioning the respective chapters where more details can be found. This part also includes discussion of methods used for hyperparameter optimization and workflow design. Part II includes the discussion of more advanced techniques and methods. The first chapter discusses the problem of setting up configuration spaces and conducting experiments. Subsequent chapters discuss different types of ensembles, metalearning in ensemble methods, algorithms used for data streams and transfer of meta-models across tasks. One chapter is dedicated to metalearning for deep neural networks. The last two chapters discuss the problem of automating various data science tasks and trying to design systems that are more complex. Part III is relatively short. It discusses repositories of metadata (including experimental results) and exemplifies what can be learned from this metadata by giving illustrative examples. The final chapter presents concluding remarks.