K-Means: Implementation and Analysis

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

Unsupervised Learning: Dimensionality Reduction and Clustering

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.

Mood disorders and urban environment

Delves into the relationships between mood disorders, cognitive performance, and brain plasticity in urban environments, using data from medical cohorts.

Clustering & Density Estimation

Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.

Unsupervised Behavior Clustering

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

Environmental Legislation and Noise Protection

Explores environmental legislation, noise protection, and exposure limits for road traffic and aircraft noise.

Environmental Legislation and Construction Law

Explores environmental legislation, pollution control, noise regulations, and construction impact on the environment.

K-Means Clustering: Image Compression

Covers K-means algorithm for image compression and PCA for dimensionality reduction.

Dimensionality Reduction: PCA & t-SNE

Explores PCA and t-SNE for reducing dimensions and visualizing high-dimensional data effectively.

Land Use and Environmental Law in Switzerland

Discusses Swiss land use and environmental law, focusing on emission control and legal obligations for protecting against nuisances.