Basic concepts

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- Clustering

- K means clustering
- Machine Learning Clustering

- Anomaly detection

- Nonnegative matrix factorization
- Deep Unsupervised Learning

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Applications of clustering, data preparation, similarity measures, k-means algorithm, quality of clustering

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Generative adversarial networks, variational autoencoders, autoregressive models, flow models, energy based models, compression, self-supervised learning, semi-supervised learning

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The task of partitioning the dataset into groups, called clusters the goal is to split up the data ins such a way that points within a single cluster are very similar and points in a different cluster are different

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A matrix V is factorized into two matrices W and H, can be used in computer vision, for topic modelling in document clustering, recommender systems

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k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster

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Task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters)

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The identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data

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Dimensionality reduction, statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components, condense the information of a large set of correlated variables into a few variables

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Applications of clustering, data preparation, similarity measures, k-means algorithm, quality of clustering

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Generative adversarial networks, variational autoencoders, autoregressive models, flow models, energy based models, compression, self-supervised learning, semi-supervised learning

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