Artificial Intelligence (AI) algorithms are the backbone of modern AI systems. They enable machines to learn from data, make decisions, and perform tasks that would be difficult or impossible for humans. This page provides an overview of various AI algorithms and their applications.

Common AI Algorithms

  • Supervised Learning: Algorithms that learn from labeled data. Examples include:

    • Linear Regression
    • Logistic Regression
    • Support Vector Machines (SVM)
    • Random Forest

  • Unsupervised Learning: Algorithms that learn from unlabeled data. Examples include:

    • Clustering (K-means, Hierarchical Clustering)
    • Association Rules (Apriori Algorithm)
    • Dimensionality Reduction (PCA)

  • Reinforcement Learning: Algorithms that learn from interactions with an environment. Examples include:

    • Q-Learning
    • Deep Q-Network (DQN)
    • Policy Gradient

Applications of AI Algorithms

AI algorithms are used in various fields, including:

  • Healthcare: Predicting disease outbreaks, diagnosing medical conditions, and personalized medicine.
  • Finance: Fraud detection, algorithmic trading, and credit scoring.
  • Transportation: Autonomous vehicles, traffic management, and route optimization.
  • Retail: Customer segmentation, recommendation systems, and inventory management.

Learn More

For more detailed information on AI algorithms, visit our AI Algorithms Tutorial.

AI Algorithms