2 Section 1 - Introduction to Machine Learning Overview

In the Introduction to Machine Learning section, you will be introduced to machine learning.

After completing this section, you will be able to:

This section has one part: introduction to machine learning.

2.1 Notation

There is a link to the relevant section of the textbook: Notation

Key points

\(X_1, ..., X_p\) denote the features, \(Y\) denotes the outcomes, and \(\hat{Y}\) denotes the predictions.
Machine learning prediction tasks can be divided into categorical and continuous outcomes. We refer to these as classification and prediction, respectively.

There is a link to the relevant section of the textbook: An Example

Key points

\(Y_i\) = an outcome for observation or index i.
We use boldface for \(\mathbf{X_i}\) to distinguish the vector of predictors from the individual predictors \(X_{i,1}, ..., X_{i,784}\).
When referring to an arbitrary set of features and outcomes, we drop the index i and use \(Y\) and bold \(\mathbf{X}\).
Uppercase is used to refer to variables because we think of predictors as random variables.
Lowercase is used to denote observed values. For example, \(\mathbf{X} = \mathbf{x}\).

True or False: A key feature of machine learning is that the algorithms are built with data.

True or False: In machine learning, we build algorithms that take feature values (X) and train a model using known outcomes (Y) that is then used to predict outcomes when presented with features without known outcomes.