Glossary: part of writing assignment for Week 2

Begin collecting terms that we can define in a glossary in our book. "Terms" might also mean abbreviations and symbols. For example, we might want to define the meaning of some the symbols used in mathematical expressions that we encounter in our reading. 

Here is a start of a list of terms that you might want to include in our glossary:

• supervised learning
  
• unsupervised learning
  
• linear regression
  
• method of least squares
  
• feature (feature set)
  
• training set
  
• classification
  
• clustering
  
• deep learning
• neural network
• k-nn (k nearest neighbors)
  
• naive Bayes method