Abtsract: Artificial Intelligence (AI) systems are increasingly deployed in complex scenarios that require both learning from data and reasoning with structured knowledge. Neuro-Symbolic (NeSy) AI has emerged as a promising paradigm to bridge these two dimensions by integrating perception on raw data with symbolic reasoning. This course focuses on NeSy approaches for sequential domains, where logical dependencies unfold over time and are naturally captured by formalisms such as Deterministic Finite Automata (DFAs) and Linear Temporal Logic (LTL). We will review recent advancements in NeSy integration for sequential domains, with a particular emphasis on non-Markovian Reinforcement Learning, sequence generation, and automata induction. Covered topics include NeSy Reward Machines, deep learning for sequential data, automata learning, symbol grounding, transfer learning across LTL tasks, LTL and natural language, integrating LTL into generative AI, and more.