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Clustering Methods
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Related lectures (33)
Unsupervised Learning: PCA & K-means
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Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.
Machine Learning: Supervised and Unsupervised Learning Techniques
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Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.
Clustering & Density Estimation
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Covers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.
Clustering Methods
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Covers K-means, hierarchical, and DBSCAN clustering methods with practical examples.
Dimensionality Reduction: PCA & t-SNE
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Explores PCA and t-SNE for reducing dimensions and visualizing high-dimensional data effectively.
K-means Algorithm
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Covers the K-means algorithm for clustering data samples into k classes without labels, aiming to minimize the loss function.
Machine Learning Fundamentals
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Introduces machine learning basics, performance metrics, optimization techniques, and model evaluation.
Clustering: K-Means
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Covers clustering and the K-means algorithm for partitioning datasets into clusters based on similarity.
Classification: Introduction
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Covers clustering and classification, building models to assign objects to classes based on attribute values.
Dimensionality Reduction: PCA and LDA
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Covers dimensionality reduction techniques like PCA and LDA, clustering methods, density estimation, and data representation.
Statistical Signal & Data Processing
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Explores Statistical Signal & Data Processing fundamentals, tools, and applications in various fields.
Brain Intelligence: Continual Learning of Representational Models
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Delves into the continual learning of representational models in brain intelligence, emphasizing rapid adaptation to unstructured environments.
Quantization of Probability Distributions
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Covers quantization of probability distributions, statistical k-means clustering, mean estimation, robust clustering methods, and open research questions.
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