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Related lectures (31)

Introduction: ESDA in environmental health

Introduces exploratory data analysis in environmental health, covering its definition, techniques, and applications in georeferenced health data analysis.

Causal Discovery: Latent Variable Models

Explores causal discovery using latent variable models, emphasizing the challenges and solutions in inferring causal relationships from non-Gaussian data.

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.

Principal Components: Properties & Applications

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

Kernel PCA: Nonlinear Dimensionality Reduction

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

Principal Component Analysis: Applications and Limitations

Explores the applications and limitations of Principal Component Analysis, including denoising, compression, and regression.

Knowledge Tracing: Student Learning and Latent Variables

Explores Bayesian Knowledge Tracing and latent variables for tracing student knowledge and making predictions.

Canonical Correlation Analysis: Linear and Kernel CCA

Introduces Canonical Correlation Analysis for finding joint features in separate datasets, extending to multimodal data and non-linear features.

Graph metrics: Statistical analysis

Explores graph metrics and statistical analysis in network clustering, including ERGMs application in sociology and asymptotics.

Dimensionality Reduction: PCA & t-SNE

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

Applications of Kernel CCA

Covers the diverse applications of Kernel Canonical Correlation Analysis, showcasing its effectiveness in various domains.

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