Unsupervised learning:
In this learning mode, the input data is marked, while the input data is not identified. This learning model can be used for prediction, however, the model first needs to learn the internal structure of the data to organize the data reasonably for prediction. Application scenarios include classification and regression. algorithms include extensions of common supervised learning algorithms. These algorithms first attempt to model unlabeled data and then predict the labeled data. Graph inference or Laplacian SVM.
Reinforcement Learning
In this learning mode, the input data is used as the feedback to the model. Unlike the supervised model, the input data is only used as a method to check whether the model is correct or wrong. In reinforcement learning, input data is directly fed back to the model, and the model must be adjusted immediately. Common application scenarios include dynamic systems and robot control. Common algorithms include Q-learning and temporal difference learning ).
In enterprise data application scenarios, supervised learning and unsupervised learning are the most common models. In image recognition and other fields, semi-supervised learning is a hot topic due to the existence of a large amount of unlabeled data and a small amount of identifiable data. Reinforcement Learning is more applied in Robot Control and other fields that require system control.
Unsupervised learning and Reinforcement Learning for Machine Learning