Week 5

This week we learned about the different types of machine learning such as supervised learning, unsupervised learning, and reinforcement learning. We also learned about the naive bayes classifier.

Supervised learning, in the context of artificial intelligence (AI) and machine learning, is a type of system in which both input and desired output data are provided. Input and output data are labelled for classification to provide a learning basis for future data processing.

Unsupervised learning is the training of an artificial intelligence (AI) algorithm using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance. Unsupervised learning algorithms can perform more complex processing tasks than supervised learning systems.

The Naive Bayes is a classification algorithm that is suitable for binary and multiclass classification. Naïve Bayes performs well in cases of categorical input variables compared to numerical variables. It is useful for making predictions and forecasting data based on historical results.

We also did exercises using naive bayes classifier to make a prediction with the given data.