• Supervised learning: inferring a function from labeled training data
• Supervised learning: predictor measurements associated with a response measurement; we wish to fit a model that relates both for better understanding the relation between them (inference) or with the aim to accurately predicting the response for future observations (prediction)
• Supervised learning: support vector machines, neural networks, linear regression, logistic regression, extreme gradient boosting
• Supervised learning examples: predict the price of a house based on the are, size.; churn prediction; predict the relevance of search engine results.
• Unsupervised learning: inferring a function to describe hidden structure of unlabeled data
• Unsupervised learning: we lack a response variable that can supervise our analysis
• Unsupervised learning: clustering, principal component analysis, singular value decomposition; identify group of customers
• Unsupervised learning examples: find customer segments; image segmentation; classify US senators by their voting.

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