Extreme Value Theory: Return Level Estimation

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Extreme Value Time Series: Modelling and Dependence

Explores extremal limit theorems, point processes, and multivariate extremes in extreme value time series modelling, emphasizing the effect of local dependence on extreme values.

Extreme Value Theory: Clustering

Explores extremal index, clustering in extreme events, return levels, and statistical models for analyzing extremes in time series.

Extreme Value Analysis: Applications and Consequences

Explores extremal limit theorems and statistical analysis for analyzing extreme events like Venezuela rainfall and Venice data.

Poisson Processes and Angular Density

Explores Poisson processes, Pickands' function, and multivariate angular densities.

Extreme Value Theory: Maxima and Exceedances

Delves into Extreme Value Theory, discussing maxima, exceedances, and their statistical implications.

Simulation & Optimization: Poisson Process & Random Numbers

Explores simulation pitfalls, random numbers, discrete & continuous distributions, and Monte-Carlo integration.

Extreme Statistics: Threshold Models

Covers the theory and applications of extreme statistics, focusing on threshold models for analyzing extremes of time series.

Unsupervised Learning: Clustering

Explores unsupervised learning through clustering techniques, algorithms, applications, and challenges in various fields.

Supervised Learning Overview

Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.

Clustering & Density Estimation

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