An AI machine learning method that trains a neural network by example. Supervised learning feeds the network predefined and labeled inputs in both the training and fine tuning stages of the model.

Semi-supervised learning merges supervised and unsupervised methods, enhancing data analysis. This approach uses less labeled data, making it cost-effective yet precise in pattern recognition.