Nonparametric Network Summaries

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Related lectures (32)

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Recommender Systems and Structure Discovery

Explores recommender systems, latent factor models, and clustering algorithms for structure discovery.

Clustering Methods

Covers K-means, hierarchical, and DBSCAN clustering methods with practical examples.

Structure Discovery: Tracing Student Knowledge

Introduces Bayesian Knowledge Tracing, Additive Factors Model, and clustering algorithms for tracing student knowledge and discovering structures.

Dimensionality Reduction: PCA and LDA

Covers dimensionality reduction techniques like PCA and LDA, clustering methods, density estimation, and data representation.

Unsupervised Learning: Clustering

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

Introduction to Clustering: Methods and Applications

Covers the fundamentals of clustering in unsupervised learning and its practical applications.

Graphical Models: Representing Probabilistic Distributions

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Graph Coloring II

Explores advanced graph coloring concepts, including planted coloring, rigidity threshold, and frozen variables in BP fixed points.

Social Networks: Weak Ties and Triadic Closure

Explores weak ties' significance in social networks, job finding, and information diffusion, emphasizing the Triadic Closure principle and the paradox of weak ties in job finding.

Clustering & Density Estimation

Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.