AI Learning Types - Unsupervised Learning

Unsupervised learning involves training AI systems on unlabelled data, without any predefined input-output pairs. The AI system learns to identify patterns, correlations, or structures within the data by itself.

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The difference between Supervised and Unsupervised learning

Supervised learning is a powerful technique, but it comes with a significant drawback - it needs a lot of labelled data. For instance, if you want to train an AI system to identify spoons, you might need to provide it with 1,000 or even 10,000 pictures of spoons That's a massive amount of spoon photos to input into the system!

However, if you consider how humans learn, the comparison is quite striking. If you've ever taught a child what a spoon is, you probably didn't show them 10,000 unique spoons. Yet, they can still recognize a spoon when they see one. So, in this respect, AI systems currently require a lot more data to learn than humans or most animals.

This is why many AI researchers are excited about the potential of unsupervised learning. They believe it could be a way for AI to learn more effectively and more naturally, like humans or animals, but with much less labelled data in the future.

Unsupervised learning is useful for tasks such as anomaly detection, clustering, and dimensionality reduction. Its often easier to understand these concepts with an example.

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Clustering example

Imagine you're running a bookshop in a bustling part of town. Your inventory includes a diverse range of books, from bargain paperbacks to high-end, collector's editions. To gain a better understanding of your customers' purchasing patterns, you start to track their purchases. Specifically, you note the number of books each customer buys and the average price per book they pay.

After analysing this data, you spot two distinctive patterns, or "clusters".

The first cluster consists of customers who buy many low-cost paperbacks. If your shop is in a busy commuting area, this group might represent daily commuters who enjoy catching up on reading during their travel, but don't want to spend too much on each book.

The second cluster includes customers who purchase fewer books but opt for the more expensive, collector's editions. This group could represent book enthusiasts or collectors who don't buy as many books but are willing to spend more on each purchase for a special edition.

These clusters, identified by an unsupervised learning algorithm, provide valuable insights for market segmentation. By understanding these customer behaviours, you can tailor your marketing strategy. For instance, you could target special offers on paperbacks at daily commuters, while promoting collector's editions to book enthusiasts. This strategy would cater to each group's specific buying preferences, enhancing your shop's appeal and potentially boosting sales.

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