Single cell RNA-sequencing: Methods and Applications

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Unsupervised Machine Learning: Clustering Basics

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

Introduction to Clustering: Methods and Applications

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

Recommender Systems and Structure Discovery

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

Clusters and Communities

Explores clustering, community detection, K-means, GMM, modularity, and the Louvain method.

Clustering: Dimensionality Reduction

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

Clustering Techniques: K-means and DBSCAN

Explores k-means and DBSCAN clustering techniques, covering data point assignment and classification types.

Structure Discovery: Tracing Student Knowledge

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

Clustering: K-means & LDA

Covers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.

Graph Coloring II

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

Clustering: Unsupervised Learning

Explores dimensionality reduction, clustering algorithms, and the state of machine learning.