This paper provides an overview of Multi-Task Learning (MTL), a promising area in machine learning that aims to improve the performance of multiple related learning tasks by leveraging useful information among them. The authors define MTL and discuss various settings, including multi-task supervised learning, unsupervised learning, semi-supervised learning, active learning, reinforcement learning, online learning, and multi-task multi-view learning. For each setting, representative MTL models are presented, and parallel and distributed MTL models are introduced to address large-scale and distributed data. The paper also reviews applications of MTL in areas such as computer vision, bioinformatics, health informatics, speech, natural language processing, web applications, and ubiquitous computing. Finally, recent theoretical analyses of MTL are discussed, providing insights into the generalization performance of MTL models.This paper provides an overview of Multi-Task Learning (MTL), a promising area in machine learning that aims to improve the performance of multiple related learning tasks by leveraging useful information among them. The authors define MTL and discuss various settings, including multi-task supervised learning, unsupervised learning, semi-supervised learning, active learning, reinforcement learning, online learning, and multi-task multi-view learning. For each setting, representative MTL models are presented, and parallel and distributed MTL models are introduced to address large-scale and distributed data. The paper also reviews applications of MTL in areas such as computer vision, bioinformatics, health informatics, speech, natural language processing, web applications, and ubiquitous computing. Finally, recent theoretical analyses of MTL are discussed, providing insights into the generalization performance of MTL models.