Inductive learning describes smart algorithms that learn from a set of instances to draw conclusions. In statistical ML, k-nearest neighbor and support vector machine are good examples of inductive learning.

There are three literals in (top-down) inductive learning:

- Arithmetic literals
- Equality and inequality
- Predicates

In deductive learning, the smart algorithms draw conclusions by following a truth-generating structure (major premise, minor premise, and conclusion) and then improve them based on previous decisions. In this scenario, the ML algorithm engages in deductive reasoning using a decision tree.

Abductive learning is a DL technique where conclusions are made based on various instances. With this approach, inductive reasoning is applied to causal relationships in deep neural networks.