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This book offers a comprehensive exploration of multi-task learning (MTL), a pivotal paradigm in modern machine learning that emphasizes learning related tasks together rather than in isolation. By sharing representations and inductive biases, MTL can enhance data efficiency and generalization, yet it also presents challenges such as task interference and scalability. This volume provides a coherent introduction to these issues, presenting diverse perspectives and applications across science and engineering.

Key concepts include shared representations, parameter sharing in neural networks, and task-relatedness measures. The chapters delve into both classical and contemporary MTL ideas, covering topics like regularized formulations, gradient conflicts, and structured data. Readers will encounter discussions on federated systems, healthcare applications, and geoscience, illustrating MTL's versatility and impact.

This book is an essential resource for researchers, practitioners, and students in machine learning and related fields. It serves as both an introduction for newcomers and a reference for those already engaged in MTL research. By highlighting conceptual foundations and practical applications, the book encourages the thoughtful adoption of MTL and inspires further investigation into its potential to transform learning paradigms.