Extreme Value Theory: Return Level Estimation

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Explores extremal limit theorems, statistical analysis, and applications of extremal processes in various fields, focusing on modeling extreme events and fitting suitable models.

Asymptotic Independence Models

Explores extremal limit theorems, statistical analysis, and asymptotic independence models for rare events.

Clustering Methods

Covers K-means, hierarchical, and DBSCAN clustering methods with practical examples.

Unsupervised Learning: PCA & K-means

Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.

Clustering: Unsupervised Learning

Explores clustering in high-dimensional space, covering methods like hierarchical clustering, K-means, and DBSCAN.

Unsupervised Behavior Clustering

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

Extreme Value Theory: Applications to Time Series

Explores Extreme Value Theory applications to time series, discussing extremogram, moving maxima, and rare event threshold sequences.

Spin Glasses and Bayesian Estimation

Covers the concepts of spin glasses and Bayesian estimation, focusing on observing and inferring information from a system closely.

Unsupervised Learning: Clustering Methods

Covers unsupervised learning focusing on clustering methods and the challenges faced in clustering algorithms like K-means and DBSCAN.

Clustering Methods and Dimensionality Reduction

Covers clustering methods and dimensionality reduction techniques.