Introduction to Machine Learning

Machine learning is a subfield of artificial intelligence that allows computers to learn without being explicitly programmed.

• True

• False

A categorical attribute assumes values that are integer or real.

• True

• False

A dataset is a collection of data examples and their attributes.

• True

• False

An unsupervised learning algorithm receives no supervised output labels but uses delayed reward and punishment to learn better actions.

• True

• False

The machine learning stages are training and testing.

• True

• False

Artificial intelligence is a branch of computer science emphasizing the simulation of human intelligence.

• True

• False

The output attributes of regression are:

• categorical values

• numeric values

• categorical or numeric values

• None of the above

Three types of machine learning are:

• deep learning, moderate learning, and shallow learning

• human learning, animal learning, and robotics learning

• visual learning, reading learning, and writing learning

• supervised learning, unsupervised learning, and reinforcement learning

Supervised learning can be divided into:

• classification and clustering

• classification and regression

• classification, clustering, and regression

• clustering and regression

What is the goal of clustering?

• Group similar instances

• Predict numerical values for testing examples

• Reduce the number of features

• None of the above