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Related lectures (13)
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Introduction to Structural Mechanics
Introduces structural mechanics concepts like distributed loads, centroids, and equilibrium in 2D and 3D.
Closest Vector Problem: Voronoi Cells
Explores the Closest Vector Problem and Voronoi cells in lattice reduction algorithms.
K-means Clustering: Lloyd's Algorithm and RGB Space
Explains K-means clustering with Lloyd's algorithm and RGB space for color segmentation.
Image Processing I: Quantization and Histogram Analysis
Explores image quantization, histogram analysis, and the trade-off between spatial and grayscale resolution.
Clustering: Hierarchical and K-means Methods
Introduces hierarchical and k-means clustering methods, discussing construction approaches, linkage functions, Ward's method, the Lloyd algorithm, and k-means++.
Closest Vector Problem: Voronoi Cells
Explores the closest vector problem in lattices and the role of Voronoi cells in determining the closest vector.
K-means Algorithm: Demo
Introduces the k-means algorithm for clustering data points based on centroids and iterative updates.
K-Means: Implementation and Analysis
Covers the implementation and analysis of the K-Means algorithm, including centroid initialization, point assignment, and noise impact.
K-Means Clustering: Image Compression
Covers K-means algorithm for image compression and PCA for dimensionality reduction.
Clustering Techniques: K-means and DBSCAN
Explores k-means and DBSCAN clustering techniques, covering data point assignment and classification types.
K-Means Algorithm: Implementation and Application as Recommendation System
Delves into implementing the K-Means algorithm, evaluating performance, and applying it as a recommendation system.
Clustering: k-means
Explains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.
Kernel K-Means Method
Introduces the kernel k-means method to form non-convex clusters and discusses clustering by density to identify dense regions in datasets.
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