Causal inference
Causal inference is a field of study that focuses on identifying and establishing cause-and-effect relationships between variables.
data analysis
K-means clustering
K-means clustering is a widely used algorithm in data analysis and machine learning for partitioning a dataset into distinct groups based on feature similarity.
Multidimensional scaling (MDS)
Multidimensional scaling (MDS) is a statistical technique used for visualizing the level of similarity of individual cases in a dataset.
Hidden Markov model (HMM)
A Hidden Markov Model (HMM) is a statistical model that represents systems with hidden states. It is widely used in various applications such as speech recognition and bioinformatics.
Isomap
Isomap is a manifold learning technique used for dimensionality reduction while preserving geodesic distances between points in a dataset.
Latent Dirichlet allocation (LDA)
Latent Dirichlet Allocation (LDA) is a generative statistical model used for topic modeling in natural language processing and machine learning.
Canonical correlation analysis (CCA)
Canonical correlation analysis (CCA) is a statistical method used to understand the relationships between two multivariate datasets. It identifies linear combinations of variables that are maximally correlated.
Unsupervised learning
Unsupervised learning is a machine learning paradigm that analyzes data without pre-labeled responses to identify patterns and structures.
Locally linear embedding (LLE)
Locally Linear Embedding (LLE) is a nonlinear dimensionality reduction technique used for data visualization and analysis in machine learning.
Spectral clustering
Spectral clustering is a technique in machine learning that uses eigenvalues of a similarity matrix to reduce dimensionality before clustering data points.