The video explains the concept of linear models in machine learning, highlighting the difference between linear and affine functions. It discusses how a constant addition affects linearity and how such models are termed in machine learning literature. Despite mathematical definitions, affine models are often referred to as linear in machine learning, emphasizing the importance of understanding these terms when reading academic papers and codes.

What is the main focus of the initial exercise discussed in the video?

• To explore non-linear models

• To determine if a model is linear in parameters

• To calculate the output of a model

• To understand matrix multiplication

What happens when a constant is added to a linear model?

• It becomes a non-linear model

• It remains a linear model

• It becomes a quadratic function

• It becomes an affine function

How are affine functions often referred to in machine learning literature?

• As exponential models

• As linear models

• As non-linear models

• As quadratic models

Why might there be confusion about the term 'linear' in machine learning?

• Because it ignores constants

• Because it includes quadratic terms

• Because it includes affine functions

• Because it only applies to non-linear models

Why is it important to understand the distinction between linear and affine models in machine learning?

• To understand literature and terminologies

• To avoid errors in coding

• To improve computational efficiency

• To correctly apply mathematical definitions