One of the grand goals of Artificial Intelligence (AI) is building an artificial “continual learning” agent that constructs a sophisticated understanding of the world from its own experience through the autonomous incremental development of ever more complex knowledge and skills. However, continual learning and adaptation capabilities, while more than often thought as fundamental pillars of every intelligent agent, have been mostly left out of the main AI research focus. In this PhD course, we will study the application of these ideas in light of the more recent advances in machine learning research and in the context of deep architectures for AI. We will start with an introduction to the topic, highlighting its relationship with related
research areas and a comprehensive guide to the founding concepts of continual learning. The main portion of the course, instead, will be dedicated to the introduction to state-of-the-art benchmarks, strategies, and evaluation methodologies. The third and final part of the course will concern practical recommendation, real-world applications, and interesting research directions for the future of this exciting research topic.