It depends on whether the number of parameters in the model grows as the number of examples in the dataset grows. Here, parameter means the entity that measures the model like median.
Parametric :Fixed number of parameters regardless of the number of examples.
- Liner Regression
- Logistic Regression
- Linear Discriminant Analysis
- Perceptron
- Naive Bayes
Non-Parametric: Not fixed number of parameters regardless of the number of examples.
- k-Nearest Neighbors
- Decision Trees like CART and C4.5
- Support Vector Machines
- Neural Network( Debatable as it is building..)