"Unsupervised learning is a paradigm in machine learning where, in contrast to supervised learning and semi-supervised learning, algorithms learn patterns exclusively from unlabeled data."
Learning from unlabelled data, including clustering, dimensionality reduction, and anomaly detection.
Clustering: Grouping of similar data points without any prior knowledge of labels or classes.
Dimensionality reduction: Reducing the number of features or variables in the dataset while retaining the most important information.
Anomaly detection: Identifying unusual or unexpected data points or patterns that deviate from the normal behavior of the dataset.
Density estimation: Estimating the probability density function of the underlying distribution of the data.
Generative models: Models that learn the underlying distribution of the data and can generate new samples from this distribution.
Autoencoders: Neural networks used to learn a compressed representation of the input data.
Reinforcement learning: Learning through trial and error in an environment without supervision.
Principal component analysis (PCA): A statistical technique used for dimensionality reduction.
Gaussian mixture models: Models that represent the data as a mixture of several Gaussian distributions.
K-means clustering: A common clustering algorithm that separates the data into k number of clusters based on similarity.
Self-organizing maps: A neural network-based clustering algorithm that displays the clusters in a 2D or 3D visualization.
Hierarchical clustering: A clustering algorithm that creates a hierarchy of clusters started from a single cluster.
Expectation-maximization algorithm: Algorithm for finding maximum likelihood estimates of parameters in probabilistic models, especially Gaussian mixture models.
Similarity/distance metrics: Measures of similarity or dissimilarity between data points used in clustering algorithms.
Non-negative matrix factorization: Matrix factorization technique used for dimensionality reduction and clustering.
Clustering: Clustering is a type of unsupervised learning that involves grouping data points together based on their similarity. The objective of clustering is to create clusters that are internally homogeneous and externally heterogeneous.
Anomaly Detection: Anomaly detection is the process of identifying rare or unusual data points in a dataset. The objective of anomaly detection is to identify data points that do not fit the expected pattern of the data.
Dimensionality Reduction: Dimensionality reduction is a technique used to reduce the number of dimensions in a dataset. This is done by identifying and removing irrelevant or redundant features.
Association Rules: Association rules is a technique used to identify patterns in data. The objective of association rules is to identify sets of data points that frequently co-occur in the dataset.
Neural Networks: Neural networks is a type of unsupervised learning that involves building a network of interconnected nodes that can learn to recognize patterns in data.
Deep Learning: Deep learning is a type of unsupervised learning that involves training neural networks with large amounts of data.
Autoencoders: Autoencoders is a type of unsupervised learning that involves building a network of interconnected nodes that can learn to compress and reconstruct data.
Generative Adversarial Networks (GANs): GANs is a type of unsupervised learning that involves training two neural networks to compete against each other. The objective of GANs is to generate new, synthetic data that is similar to the real data.
Self-organizing Maps: Self-organizing maps is a type of unsupervised learning that involves building a two-dimensional map of data points. The objective of self-organizing maps is to identify the underlying structure of the data.
Hierarchical Clustering: Hierarchical clustering is a type of unsupervised learning that involves building a tree-like structure of clusters. The objective of hierarchical clustering is to identify nested clusters in the data.
"Unsupervised learning is a paradigm in machine learning where, in contrast to supervised learning and semi-supervised learning, algorithms learn patterns exclusively from unlabeled data."
"algorithms learn patterns exclusively from unlabeled data."
"...algorithms learn patterns exclusively from unlabeled data."
"Unsupervised learning is a paradigm in machine learning where, in contrast to supervised learning and semi-supervised learning, algorithms learn patterns exclusively from unlabeled data."