Entropy

Unsupervised and Reinforcement Learning Algorithms

Unlike supervised learning, unsupervised learning deals with unlabeled data, meaning the algorithm must find hidden structures and patterns without predefined outputs. This approach is widely used in clustering, anomaly detection, and dimensionality reduction.

Clustering Algorithms

Clustering is a technique used to group similar data points based on shared characteristics. Some of the most commonly used clustering algorithms include:

• K-Means Clustering – A widely used clustering algorithm that partitions data into K clusters based on feature similarity. It is commonly applied in market segmentation and image segmentation.
• Hierarchical Clustering – This method builds a hierarchy of clusters and does not require specifying the number of clusters in advance. It is often used in biological data analysis.
• DBSCAN (Density-Based Spatial Clustering) – Unlike K-Means, DBSCAN does not require specifying the number of clusters. It identifies dense regions in data and is effective in anomaly detection.

Dimensionality Reduction Algorithms

When working with high-dimensional datasets, reducing the number of features while retaining the most relevant information is crucial. Some important dimensionality reduction techniques include:

• Principal Component Analysis (PCA) – A technique that transforms high-dimensional data into a smaller set of uncorrelated components while preserving variance.
• t-Distributed Stochastic Neighbor Embedding (t-SNE) – A non-linear technique for visualizing high-dimensional data in two or three dimensions.
• Autoencoders – A type of neural network used for unsupervised learning that compresses data into a latent space and reconstructs it to minimize information loss.

Reinforcement Learning Algorithms

Reinforcement Learning (RL) is a different paradigm in which an agent learns by interacting with an environment and receiving rewards or penalties. It is widely used in robotics, gaming, and autonomous systems.

• Q-Learning – A model-free RL algorithm that learns the best action-selection policy using a Q-table.
• Deep Q-Networks (DQN) – A neural network-based extension of Q-Learning that improves learning in complex environments.
• Policy Gradient Methods – Algorithms like REINFORCE that learn optimal policies directly instead of using value-based learning.
• Actor-Critic Methods – A combination of value-based and policy-based RL that improves learning efficiency.

Unsupervised learning and reinforcement learning play crucial roles in fields like **recommendation systems, robotics, and financial modeling**. These techniques allow machines to learn autonomously, making them indispensable for future AI advancements.