Biclustering: Networks MA448

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Related lectures (32)

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Clustering Methods

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

Clustering: k-means

Explains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.

Linear Algebra: Matrices Properties

Explores properties of 3x3 matrices with real coefficients and determinant calculation methods.

Computational Methods: Paths and Strings

Covers computational methods focusing on paths and strings, including examples of concatenation, regex elements, and string operations.

Machine Learning: Fundamentals and Applications

Introduces machine learning basics, covering data segmentation, clustering, classification, and practical applications like image classification and face similarity.

Unsupervised Learning: Clustering & Dimensionality Reduction

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Machine Learning Fundamentals

Covers key concepts and examples of machine learning algorithms and techniques.

Algorithmic Paradigms for Dynamic Graph Problems

Covers algorithmic paradigms for dynamic graph problems, including dynamic connectivity, expander decomposition, and local clustering, breaking barriers in k-vertex connectivity problems.

Unsupervised Behavior Clustering

Explores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.

Kernel K-means Clustering

Explores Kernel K-means clustering, interpreting solutions, handling missing data, and dataset selection for machine learning.