This paper introduces a model for automatically learning inductive bias in machine learning. The key idea is that a learner embedded in an environment of related learning tasks can sample from multiple tasks to find a hypothesis space that performs well on many of them. This approach allows the learner to generalize better to novel tasks within the same environment. The model is based on the PAC learning framework and extends it to handle multiple related tasks. The main results show that learning multiple tasks reduces the sample complexity required for good generalization and that a hypothesis space learned from many tasks is likely to perform well on new tasks. The paper also provides bounds on the number of training tasks and examples needed to ensure good generalization. It discusses the implications of this model for various learning problems, including neural network feature learning, and compares it with existing approaches in the literature. The paper concludes that learning bias through multiple related tasks can lead to better generalization than traditional methods.This paper introduces a model for automatically learning inductive bias in machine learning. The key idea is that a learner embedded in an environment of related learning tasks can sample from multiple tasks to find a hypothesis space that performs well on many of them. This approach allows the learner to generalize better to novel tasks within the same environment. The model is based on the PAC learning framework and extends it to handle multiple related tasks. The main results show that learning multiple tasks reduces the sample complexity required for good generalization and that a hypothesis space learned from many tasks is likely to perform well on new tasks. The paper also provides bounds on the number of training tasks and examples needed to ensure good generalization. It discusses the implications of this model for various learning problems, including neural network feature learning, and compares it with existing approaches in the literature. The paper concludes that learning bias through multiple related tasks can lead to better generalization than traditional methods.