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Lecture
Principal Components Analysis
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Related lectures (39)
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
MOOC: SES Swiss-Energyscope
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.
Mobility Planning: Theoretical Approaches
MOOC: Mobility planning
Explores theoretical approaches to mobility planning, big data, environmental impacts, and future prospects in mobility management.
Wind Energy: Technologies & Transition
MOOC: SES Swiss-Energyscope
Explores wind energy technologies, installations, benefits, and challenges, highlighting its growth in Europe and potential in Switzerland.
Hydropower: Benefits, Characteristics, Perspectives
MOOC: SES Swiss-Energyscope
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.
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.
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.
Textual Data Analysis: Classification & Dimensionality Reduction
Explores textual data classification, focusing on methods like Naive Bayes and dimensionality reduction techniques like Principal Component Analysis.
Principal Component Analysis: Dimensionality Reduction
Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.
Spectral Clustering: Theory and Applications
Explores spectral clustering theory, eigenvalue decomposition, Laplacian matrix, and practical applications in identifying clusters.
Dimensionality Reduction: PCA & Autoencoders
Explores PCA, Autoencoders, and their applications in dimensionality reduction and data generation.
Understanding Autoencoders
Explores autoencoders, from linear mappings in PCA to nonlinear mappings, deep autoencoders, and their applications.
Unsupervised Learning: Principal Component Analysis
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Covers unsupervised learning with a focus on Principal Component Analysis and the Singular Value Decomposition.
Unsupervised Learning: Clustering & Dimensionality Reduction
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Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.
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