Machine Learning: Overview

Machine Learning (ML) focuses on the using data and algorithms to enable Artificial Intelligence (AI) to imitate the way that humans learn, gradually improving its accuracy.

Machine Leaning Algorithms:  ML algorithm is not one single “black box” but several.  The commonly used algorithms are neural networks, linear Regression, logistic regression, clustering, decision trees and random forecast.

Machine Learning Methods:  This is to do with how to conduct machine learning.  There are three major categories:

• Supervised machine learning: Analysts use labeled training datasets to train algorithms to classify data or predict outcomes accurately.

• Unsupervised machine learning: Analysts apply ML algorithms to unlabeled datasets to discover hidden patterns or data groupings. The discovery is done without the need for human intervention.
• Semi-supervised learning offers a happy medium between supervised and unsupervised learning, largely due to scale and complexity of available data.

Which algorithm and learning method should be used is dependent upon applications. We will discuss some of them in detail with supply chain applications.