Most optimization algorithms, including evolutionary algorithms and metaheuristics, and general-purpose solvers for integer or constraint programming, have often many parameters that need to be properly configured (i.e., tuned) for obtaining the best results on a particular problem. Automatic (offline) algorithm configuration methods help algorithm users to determine the parameter settings that optimize the performance of the algorithm before the algorithm is actually deployed. Moreover, automatic algorithm configuration methods may potentially lead to a paradigm shift in algorithm design and configuration because they enable algorithm designers to explore much larger design spaces than by traditional trial-and-error and experimental design procedures. Thus, algorithm designers can focus on inventing new algorithmic components, combine them in flexible algorithm frameworks, and let final algorithm design decisions be taken by automatic algorithm configuration techniques for specific application contexts.This tutorial will be divided in two parts. The first part will give an overview of the algorithm configuration problem, review recent methods for automatic algorithm configuration, and illustrate the potential of these techniques using recent, notable applications from the presenters' and other researchers work. The second part of the tutorial will focus on a detailed discussion of more complex scenarios, including multi-objective problems, anytime algorithms, heterogeneous problem instances, and the automatic generation of algorithms from algorithm frameworks. The focus of this second part of the tutorial is, hence, on practical but challenging applications of automatic algorithm configuration. The second part of the tutorial will demonstrate how to tackle these configuration tasks using our irace software (http://iridia.ulb.ac.be/irace), which implements the iterated racing procedure for automatic algorithm configuration. We will provide a practical step-by-step guide on using irace for the typical algorithm configuration scenario.