Satisfiability and clusters

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Related lectures (32)

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Clustering: Unsupervised Learning

Covers clustering algorithms, evaluation methods, and practical applications in machine learning.

Clustering: Dimensionality Reduction

Explores clustering and dimensionality reduction techniques in finance to clean and simplify data.

Unsupervised Learning: Clustering

Explores unsupervised learning through clustering techniques, algorithms, applications, and challenges in various fields.

Characterisation of Clusters: Homogeneity, Separability

Explores centroid, medoid, homogeneity, separability in clustering, quality evaluation, stability, expert knowledge, and clustering algorithms.

Stochastic Block Model: Community Detection

Covers the Stochastic Block Model for community detection, focusing on the detection of communities, clusters, and groups.

Clustering & Density Estimation

Covers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.

Clustering: Theory and Practice

Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.

Unsupervised Machine Learning: Clustering Basics

Introduces unsupervised machine learning clustering techniques like K-means, Gaussian Mixture Models, and DBSCAN, explaining their algorithms and applications.

Gravitational Lensing Analysis

Covers the analysis of gravitational lensing in galaxy clusters, focusing on the mass distribution and precision achieved in the mass model of the galaxy cluster MACSJ0416.1-2403.

Molecular Complex Formation: Integrin, Talin, Kindlin

Explores molecular complex formation during cell adhesion using single-protein imaging techniques and DNA-PAINT simulations.