Unsupervised learning is a type of machine learning where the algorithm is not provided with labeled data. Instead, the model is given a dataset and must find patterns or structures within the data without explicit guidance on what those patterns should be. Here's how it works:

Key Concepts in Unsupervised Learning:

1. No Labeled Data: The dataset consists of input features, but there are no corresponding output labels as in supervised learning. The model must learn the inherent structure of the data on its own.

2. Clustering: One common task in unsupervised learning is clustering, where the model groups similar data points together. For example, in customer segmentation, the algorithm might identify distinct groups of customers based on their purchasing behaviors.

3. Dimensionality Reduction: Another task is reducing the complexity of data while preserving essential information. Techniques like PCA (Principal Component Analysis) or t-SNE help reduce dimensions in datasets, making them easier to visualize and process.

4. Association: This is about discovering relationships between variables in large datasets. A popular application is market basket analysis, where the algorithm might find that customers who buy bread are also likely to buy butter.

5. Neural Networks in Unsupervised Learning:

   • Autoencoders: A type of neural network designed for unsupervised learning. They aim to learn efficient representations of input data (such as compressing and then reconstructing it) by minimizing the difference between the original and the reconstructed input.

   • Self-Organizing Maps (SOM): Another kind of unsupervised learning network that projects high-dimensional data onto a lower-dimensional grid, helping to visualize and find patterns in the data.

   • Generative Adversarial Networks (GANs): GANs are made of two neural networks that compete with each other in a zero-sum game. One network generates data (the generator), while the other (the discriminator) evaluates the quality of the generated data. Over time, the generator improves at producing realistic data, though the training process is unsupervised.

Common Algorithms:

• K-Means Clustering: Groups data into clusters based on feature similarity.

• Hierarchical Clustering: Builds a tree of clusters for better interpretation of data.

• PCA (Principal Component Analysis): Reduces the dimensionality of data while retaining most of the variance.

• t-SNE (t-distributed Stochastic Neighbor Embedding): A technique for visualizing high-dimensional data in lower dimensions (typically 2D or 3D).

In summary, unsupervised learning networks aim to uncover patterns, relationships, or structures in data without any prior labels, making it useful for tasks where explicit categorization or supervision is not feasible.

Ali Eskici
21.09.2024