Transfer Learning: Algorithms and Applications presents an in-depth discussion on practices for transfer learning, exploring emerging fields that includes a theoretical analysis of various algorithms and problems that lay a solid foundation for future advances in the field.

In the era of Big Data, machine learning methods are widely used in natural language processing, computer vision, speech, and in signal processing communities. However, the current standard machine learning techniques, such as supervised classifiers, tend to fail when the data distribution and/or structure changes over training and test settings.

Current techniques addressing machine learning problems can only address a few isolated tasks at one time. Transfer learning, adapted from how humans learn, models the distribution and structure difference between training and test settings.