HML: Overview

How Machine Learning Works

How can machines learn? In this module, we are going to investigate how machine learning operates, gain a perspective on AI’s societal consequences, and address societal impacts of AI.

Machine Learning: Data mining, algorithm, classification, learning, neural networks, deep learning, AI, autonomous

Introduction

By now, most of us have heard about machine learning. Some of us may have a pretty good idea of what it is. A few of us may be able to explain machine learning to others. And, a small number of us may have experience in actually teaching a machine. By the end of this lesson, you will have an opportunity to learn about the different ways that machines learn and evidence your understanding by teaching a machine to learn. We will also examine societal impacts and consequences of machine learning.

Learning Questions

  1. What is machine learning?
  2. What types of machine learning are they and how do they differ?
  3. How do machines make decisions?
  4. What are the various ethical implications of AI?

Key Terms

Supervised Learning: a category of machine learning that uses labeled datasets to train algorithms to predict outcomes and recognize patterns.

Unsupervised Learning: a category of machine learning that uses uses machine learning algorithms to analyze and cluster unlabeled data sets. These algorithms discover hidden patterns or data groupings without the need for human intervention.

Reinforcement Learning: a category of machine learning that trains software to make decisions to achieve the most optimal results. It mimics the trial-and-error learning process that humans use to achieve their goals.

Structured Data: highly organized information (like information presented in a table or list: numbers, dates, categories, etc.)

Unstructured Data: lacks specific formatting or structure. Examples include text, images, videos, and audio files (…unless stated examples were captured utilizing a smart device)

Semi-structured Data: a mix of both structured and unstructured. It has some organization but doesn’t fit perfectly into tables. An example is a picture taken with a smart device. While the picture alone is considered unstructured data, the information collected by the smart device, such as time, date, location, and face recognition provide structured data.

AI Ethics: a multidisciplinary field that studies how to optimize AI's beneficial impact while reducing risks and adverse outcomes.

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