Unsupervised learning
Unsupervised machine learning is the machine learning task of inferring a function to describe hidden structure from "unlabeled" data (a classification or categorization is not included in the observations)
Resources
Sub-topics
Kernel density estimation
- https://en.wikipedia.org/wiki/Kernel_density_estimation
- kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable
- Kernel Density Estimation in Python
- Histograms and kernel density estimation KDE 2
- https://scikit-learn.org/stable/modules/density.html
- https://stackabuse.com/kernel-density-estimation-in-python-using-scikit-learn/
Dimensionality reduction and low rank modeling
See AI/Unsupervised Learning/Dimensionality reduction and low rank modeling
Clustering
See AI/Unsupervised Learning/Clustering
Blind source separation
- Blind signal separation (BSS), also known as blind source separation, is the separation of a set of source signals from a set of mixed signals, without the aid of information (or with very little information) about the source signals or the mixing process.
Independent component analysis (ICA)
- https://en.wikipedia.org/wiki/Independent_component_analysis
- In signal processing, independent component analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents. This is done by assuming that the subcomponents are non-Gaussian signals and that they are statistically independent from each other.
- https://scikit-learn.org/stable/modules/decomposition.html#ica
- https://scikit-learn.org/stable/auto_examples/decomposition/plot_ica_blind_source_separation.html
- https://mne.tools/stable/generated/mne.preprocessing.ICA.html
Anomaly and Outlier Detection
See AI/Anomaly and Outlier Detection