1.1. Artificial Intelligence and Machine Learning#
1.1.1. Artificial Intelligence#
Artificial Intelligence (AI) is a field in Computer Science that focuses on developing computers and systems to perform tasks typically associated with human intelligence. Examples of this include:
Demonstrating reasoning and decision making, e.g. to play games, or develop autonomous robots/vehicles
Recognising images, or objects in images, e.g. for surveillance
Understanding natural language, e.g. chatbots
1.1.2. Machine Learning#
Machine Learning (ML) is a subset of AI that focuses on methods that allow computers and machines to learn from data to achieve AI capabilities. In this module you’ll learn about a range of ML techniques.
These include:
linear regression
logistic regression
polynomial regression
decision trees
k nearest neighbours
clustering
neural networks
What you’ll see is that in all of these methods, the way the computer learns to ‘think’ will depend on the data it is presented with.
Note that all ML techniques are a form of AI, but not all AI is a form of ML. And example of something that may be considered AI but is not ML. For example, a simple tic-tac-toe bot that follows a pre-defined set of human-made rules, e.g.
If you can get three in a row by placing your token down somewhere, do that
If the opponent can get three in a row by placing a token down, block that
If the center is free, take the center
If the corner is free, take an corner
If the edge is free, take an edge
The reason why this is not ML is that the computer is not learning how to ‘think’ for itself, rather a human has designed how the computer thinks.
