Unsupervised Learning - Discovering Patterns Independently
Unsupervised learning is a method where only input data is provided, without any labeled answers.
The AI analyzes the given data to cluster similar items together or identify key features and summarize them.
For example, if we provide AI with various animal pictures, supervised learning would involve giving it labeled answers like "dog" or "cat" for training.
In contrast, unsupervised learning involves no answers, and AI independently groups similar images.
| Input (Image) | No Label |
|---|---|
| 🐶 Dog Image 1 | ? |
| 🐱 Cat Image 1 | ? |
| 🐶 Dog Image 2 | ? |
| 🐱 Cat Image 2 | ? |
In this way, AI can independently analyze and group dog and cat images, but it doesn't directly define what each group signifies.
Primary Types of Unsupervised Learning
Unsupervised learning can be categorized into two main types.
1. Clustering
Clustering involves automatically grouping input data based on similar features.
Through clustering, AI applications like the following can be implemented.
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Customer Segmentation: Grouping online shoppers by purchase patterns
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Music Recommendation Systems: Analyzing listening history to recommend music with similar tastes
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Social Network Analysis: Finding user groups with similar interests
2. Dimensionality Reduction
Dimensionality reduction involves retaining only significant features while reducing unnecessary information.
Dimensionality reduction enables AI applications like the following.
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Image Compression: Retaining essential features and removing unnecessary pixels
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Natural Language Processing (NLP): Extracting key words from sentences to summarize
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Gene Analysis: Summarizing gene data to keep only major characteristics
Limitations of Unsupervised Learning
While unsupervised learning is a powerful technique, it has several drawbacks.
1. Results Are Difficult to Interpret
AI may group or summarize data, but humans must interpret the meaning of these results.
2. Accuracy Is Not Guaranteed
Unlike supervised learning, there's no answer key to verify whether the model's identified patterns are meaningful.
3. Performance Varies with Data
Good results can be achieved with clear patterns, but performance may decline if data is too complex or unstructured.
To overcome these limitations, methods exist where AI not only finds patterns in data but also learns through direct action and feedback.
This is known as reinforcement learning, where AI uses trial and error to discover optimal strategies.
In the next lesson, we will explore how reinforcement learning is conducted.
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