Choose the appropriate applications of supervised learning.Handwriting recognitionClustering of newsHouse price predictionGrouping of similar web pages

The appropriate applications of supervised learning from the given options are:

1. Handwriting recognition: This is a classic example of supervised learning where the model is trained on a labeled dataset of handwriting samples and their corresponding characters. The model then uses this training to recognize and predict characters in new, unseen handwriting samples.

2. House price prediction: This is another example of supervised learning where the model is trained on a dataset of houses with various features (like size, location, number of rooms, etc.) and their corresponding prices. The model then uses this training to predict the prices of new, unseen houses based on their features.

The other two options - Clustering of news and Grouping of similar web pages - are examples of unsupervised learning, not supervised learning. In these cases, the model is not provided with any labeled data or specific outcome to predict. Instead, it must identify patterns and relationships in the data on its own in order to group similar items together.