Compare different ways in which neural networks can be used to recognize patterns

HL content: Modeling & Simulation

Introduction

There are essentially two ways neural networks can recognize patterns:

Supervised learning

Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs.

It infers a function from labeled training data consisting of a set of training examples.  In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances

Unsupervised learning

Unsupervised learning is a branch of machine learning that learns from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning identifies commonalities in the data and reacts based on the presence or absence of such commonalities in each new piece of data. Alternatives include supervised learning and reinforcement learning.

= a helpful video

This video is good to understand the difference of supervised vs unsupervised learning

From the IB

The concepts of supervised and unsupervised learning should be applied to the examples above and to any clearly explained example presented in the examination, as should the importance of genetic algorithms.

Standards

• Compare different ways in which neural networks can be used to recognize patterns

a group or system of interconnected people or things.

Obtain the only possible answer.