Supervised & Unsupervised Learning

Machine learning algorithms can be categorized into two main types: supervised learning and unsupervised learning. Each type has its own characteristics and applications in data analysis and prediction.

Supervised Learning

Supervised learning is a type of machine learning where the algorithm learns from labeled data. The goal is to learn a function that maps input variables to output variables.

Regression

Classification

Unsupervised Learning

Unsupervised learning is a type of machine learning where the algorithm learns patterns from unlabeled data. The goal is to discover hidden structures in the data.

• Used for:
  • (1) Clustering
    • Hierarchical clustering
    • K-means
    • Mixture models
    • DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
    • OPTICS (Ordering Points To Identify the Clustering Structure)
  • (2) Anomaly detection:
    • Local Outlier Factor
    • Isolation Forest
  • (3) Approaches for learning latent variable models
    • Expectation-Maximization algorithm (EM)
    • Method of moments
    • Blind signal separation techniques
      • (Principal Component Analysis, Independent Component Analysis, Non-negative Matrix Factorization, Singular Value Decomposition)
  • Dimension Reduction
    • Principal Component Analysis (PCA)
  • Association
    • Apriori algorithm
    • Partition algorithm

Generative Adversarial Networks (GANs) are also unsupervised learning

Clustering

Dimension Reduction

Association

Reinforcement Learning

Reinforcement learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize a reward signal.

• Algorithms

Additional content:

1. Semi-supervised Learning: A combination of supervised and unsupervised learning, where the algorithm learns from a dataset containing both labeled and unlabeled data.