Recommender Systems and Structure Discovery

Related lectures (31)

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

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

Compares K-Means and Spectral Clustering algorithms, highlighting their differences and practical applications in clustering student behaviors.

Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.

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

Explores unsupervised learning through clustering methods like K-means and DBSCAN, addressing challenges and applications.

Covers clustering methods and dimensionality reduction techniques.

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

Explores K-means and DBSCAN clustering methods, discussing properties, drawbacks, initialization, and optimal cluster selection.

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