Learning Methods for Machine Learning

Supervised Learning: Learning a mapping from inputs to outputs using labeled examples like classification and regression tasks.
Unsupervised Learning: Learning patterns or structure from unlabeled data like clustering and dimensionality reduction tasks.
Reinforcement Learning: Learning actions through interaction with an environment using rewards or penalties like in robotics, games and real-time decision-making tasks. Goal is to maximize payoff.

Other forms of Learning

Semi-Supervised Learning: Learning from a small amount of labeled data and a larger amount of unlabeled data.
Self-Supervised Learning: Learning representations from unlabeled data by automatically generating supervision from the data itself.

Generative Models: Models that learn to generate new data samples that resemble the training data. Examples include Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Normalizing Flows. These models learn the underlying distribution of the data.
Discriminative Models: Models that learn to distinguish between different classes or categories in the data. Examples include Logistic Regression, Support Vector Machines (SVMs), and Neural Networks. These models learn the decision boundary between different data classes.

Clustering: Grouping similar data points based on their features. Examples include K-means, DBSCAN, and hierarchical clustering.
Dimensionality Reduction: Reducing the number of features while preserving important structure or information. Examples include PCA, t-SNE, and UMAP.