## What is Machine Learning

Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed — Arthur Samuel (1959).

Tom Mitchell (1988) defined Machine Learning as a Well-Posed Learning Problem

“A computer is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.”

## Machine Learning Algorithms

Supervised Learning

Unsupervised Learning

Reinforcement Learning

### Supervised Learning

The idea of supervised learning is to provide the machine with a dataset of correct answers and allow it to predict further answers.

### Types of Supervised Learning

REGRESSION

CLASSIFICATION

A typical example of supervised learning would be the process of determining the price of a house.

### Example

Consider the table below

$200 — 100sqm

$300 — 200sqm

$500 — 300sqm

say we provide the dataset above to the supervised machine learning algorithm as input, its goal will be to predict a continuous-valued output known as regression.

#### REGRESSION

The goal of a regression problem is to predict a continuous-valued output; that is, if we provided correct answers or labeled datasets as input, the algorithm should be able to predict a value for every arbitrary input.

In the example above, the machine will predict one price value per value in sqm.

#### CLASSIFICATION

Another example of supervised learning is the process of determining the malignancy or benign of a tumor

In this example, we want to determine if the tumor found in the breast is either malignant or benign, so at every point in time, a tumor should either be malignant (X) or benign (0)

The goal of a classification problem is to predict a discrete-valued output (X or 0) at every point in time.