Extreme Value Time Series: Modelling and Dependence

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Extreme Value Theory: Return Level Estimation

Explores extremal limit theorems, return level estimation, clustering consequences, and modelling strategies for extreme value theory.

Extreme Value Theory: Clustering

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

Supervised Learning Overview

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

Unsupervised Learning: Clustering

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

Introduction to Image Classification

Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.

Graph Coloring II

Explores advanced graph coloring concepts, including planted coloring, rigidity threshold, and frozen variables in BP fixed points.

Time Series Clustering

Covers clustering time series data using dynamic time warping, string metrics, and Markov models.

Clustering & Density Estimation

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.

Clustering & Density Estimation

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

Clustering: Unsupervised Learning

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