What learning approach involves training a model on several tasks at the same time?

The correct answer is Multitask Learning (MTL) because it is specifically designed to simultaneously train a model on multiple tasks. This approach allows the model to leverage shared information and patterns across different tasks, potentially improving performance and reducing overfitting for each individual task. By optimizing the model for several related objectives at once, MTL encourages the model to learn a more generalized representation of the data, which can be beneficial in scenarios where tasks are interconnected.

In contrast to Multitask Learning, Transfer Learning involves taking a model trained on one task and fine-tuning it for another, often related task, rather than training on multiple tasks at the same time. Reinforcement Learning focuses on training agents through reward and punishment based on interactions with an environment, rather than addressing multiple tasks together. Supervised Learning primarily deals with training a model on individual tasks using labeled data, leading to a sequential rather than simultaneous approach.