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Multitask learning is one approach to address transfer learning. It uses information contained in the training signal of related tasks. Multitask learning improves performance and generalization by finding a part of feature space or transformed feature space useful for all the related tasks. To achieve this shared representation, all the related tasks are trained in parallel. From another perspective, multitask learning can be viewed as a regularization technique due to the imposed requirement of shared representation appropriate for all the related tasks. This form of regularization can be superior to other regularizers that penalize overfitting or complexity of the models. The goal of this Master’s thesis proposal is to adopt multitask learning on a vehicle-related dataset. That could include many applications such as fuel consumption, predictive maintenance, and etc.
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