  Inverse problems are paramount in Science and Engineering. In this paper, we
consider the setup of Statistical Inverse Problem (SIP) and demonstrate how
Stochastic Gradient Descent (SGD) algorithms can be used in the linear SIP
setting. We provide consistency and finite sample bounds for the excess risk.
We also propose a modification for the SGD algorithm where we leverage machine
learning methods to smooth the stochastic gradients and improve empirical
performance. We exemplify the algorithm in a setting of great interest
nowadays: the Functional Linear Regression model. In this case we consider a
synthetic data example and examples with a real data classification problem.
