Lectures related to Principal Components Analysis

Unsupervised Learning: Principal Component Analysis

Covers unsupervised learning with a focus on Principal Component Analysis and the Singular Value Decomposition.

Principal Component Analysis: Dimension Reduction

Covers Principal Component Analysis for dimension reduction in biological data, focusing on visualization and pattern identification.

Hydropower: Advantages, Classification, and Future

Explores the advantages, classification, and future prospects of Swiss hydropower, including its role as a 'battery' for energy storage.

Small Towns in France and Europe: Data-driven Approach

Examines small towns in France and Europe, focusing on their vulnerabilities and demographic challenges, particularly regarding aging populations.

Unsupervised Learning: Clustering & Dimensionality Reduction

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Wood Structures: Properties and Uses

Explores oak tree features, wood properties, naval construction, and wood modification techniques.

Europe: Geopolitical Evolution

Delves into Europe's geopolitical evolution, empires, urban networks, and economic challenges.

Mobility Planning: Theoretical Approaches

Explores theoretical approaches to mobility planning, big data, environmental impacts, and future prospects in mobility management.

Wind Energy: Technologies & Transition

Explores wind energy technologies, installations, benefits, and challenges, highlighting its growth in Europe and potential in Switzerland.

Hydropower: Benefits, Characteristics, Perspectives

Explores the benefits, features, and future prospects of Swiss hydropower, including challenges and potential by 2050.

Geotechnical Engineering: Projects and Case Studies

Explores geotechnical engineering projects, case studies, and factors influencing project outcomes.

Global Air Quality Monitoring

Explores global air quality monitoring through satellite data, covering wildfires, ship emissions, fog, Saharan dust, and fires in Europe.

Kernel PCA: Nonlinear Dimensionality Reduction

Explores Kernel Principal Component Analysis, a nonlinear method using kernels for linear problem solving and dimensionality reduction.

Singular Value Decomposition

Explores Singular Value Decomposition and its role in unsupervised learning and dimensionality reduction, emphasizing its properties and applications.

Principal Components: Properties & Applications

Explores principal components, covariance, correlation, choice, and applications in data analysis.

Diagonalization of Linear Transformations

Covers the diagonalization of linear transformations in R^3, exploring properties and examples.

Dimensionality Reduction: PCA & LDA

Covers PCA and LDA for dimensionality reduction, explaining variance maximization, eigenvector problems, and the benefits of Kernel PCA for nonlinear data.

Linear Algebra: Matrices and Operations

Introduces key concepts in linear algebra, including matrices, operations, and numerical invariants.

How Information Circulates in a Network

Covers Traceroute, Whois, 2D/3D visualization, and server locations in different continents.

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