After two "Artificial Intelligence winters",machine learning has become a subject of intense of media hype and come up in countless articles,showing a promising future.Machine learning has gained a big success in image recognition and speech recognition systems.Refining key message and dominant features from the train datasets and making accurate prediction on the never-seen-before datasets are the major task and the ultimate goal of machine learning,respectively.From this perspective,it's feasible to integrate machine learning into climate prediction.Beginning with a simple example on finding the weights of a linear fitting,this study shows how machine learning updates weights through gradient descent algorithm and eventually obtains the linear fitting line.Next,this study illustrates the architecture of neural network and uses neural network algorithm to learn the true curve fitting a non-linear function.In the end,this study elaborates the architecture of deep learning such as convolutional neural network,and uses convolutional neural network model to hindcast winter monthly surface air temperature anomalies in East Asia.The results by deep learning are further compared with the hindcast by dynamical model-CanCM4i.This study will help to understand the fundamental of machine learning and provides insights how to integrate machine learning into climate prediction.