What is it?
In Artificial Intelligence, knowledge is essential for computers to make intelligent decisions. This knowledge can be explicitly expressed (think business rules or even software programs) by humans. Alternatively, the knowledge can be learnt automatically from data via the process of induction. Machine learning is the set of techniques and algorithms that allow computers to learn (or discover) the underlying knowledge from data.
Why is it relevant?
Machine learning is already a part of our lives as we:
- shop online – products recommendation and advertisements
- bank – anomaly detection to prevent fraud
- buy insurance (health, car, home, travel) – used to determine the premium
- uploaded photos to Facebook and it automatically tags our friends – computer vision for facial recognition
- ask Siri the name of the song on the radio – speech & audio recognition
- search for anything online.
It is increasingly becoming a greater part of our lives, and the main reason for this is the growth in volume, variety, and velocity of data that our society produces. The rate at which we are generating and collecting data has been growing dramatically, commonly referred to as Big Data. Meanwhile, the cost of storing and processing data have decreased with technologies around Big Data.
There is no point in collecting and storing Big Data if we do not put it to good use. This is where computer scientists and engineers have turned to machine learning. Machine learning can help us find new insights and knowledge from the data we collect to improve existing processes or help us invent new products and services.
How does it work?
Typically, machine learning involves 2 phases – Training and Prediction.
During training, algorithms analyze the data and build a mathematical model that describes the patterns (or relationships) within the data. The training can be supervised, where the algorithms look to maximize/minimize a specific goal with labeled data. Alternatively, they can be unsupervised where algorithms focus on just describing the data as succinctly as possible. The goal is to produce the smallest and simplest model possible that describes the patterns in the data, guided by the principle of Occam’s Razor.
Once learnt, the mathematical model can then be used to understand the relationship or make predictions about the unknown (i.e. the future, new product or customer). During the prediction phase, we exploit the discovered knowledge for our goals. The goals might be to recommend products, identify the right combination of drugs suitable to help a patient or prevent the waste of water during a drought.
Further Viewing & Exploration
Here are some useful links to get you started:
- Machine Learning: Making Sense of a Messy World
- What is Machine Learning
- A Visual Intro to Machine Learning
I also retweet interesting articles on Artificial Intelligence, Machine Learning and Data Science on Twitter. Often these include tutorials, books, posts, and data. So follow me if you want to keep up.