Unsupervised learning

Unsupervised learning is a machine learning paradigm where the algorithm learns patterns and structures in data without explicit supervision or labeled outcomes. The goal is to uncover inherent relationships or groupings within the data.

Topics

• PCA (Principal Component Analysis): dimensionality reduction
• K-Means: clustering
• Linear Regression: Least Squares, Ridge and LASSO
• DBSCAN: Density-based spatial clustering of applications with noise
• GAN (Generative Adversarial Network)
• Deep autoencoder
• Restricted Boltzmann Machines (RBM)
• Recurrent Neural Network
• Expectation-Maximization Algorithm

References

• An Introduction to Statistical Learning - James, Witten, Hastie and Tibshirani #ISLP