Lectures related to Statistical distance

Adversarial Training: Theory and Applications

Covers the practical implementation and applications of adversarial training, Generative Adversarial Networks, distance between distributions, and enforcing 1-Lipschitz in GANs.

Boltzmann Machines: Generating Synthetic Data in Finance

Delves into using Boltzmann machines to generate synthetic financial data effectively.

Supervised Learning: k-NN and Decision Trees

Introduces supervised learning with k-NN and decision trees, covering techniques, examples, and ensemble methods.

Clustering: k-means

Explains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.

KNN Classifier: Nearest Neighbor Approach

Explains the K-Nearest Neighbors classifier, assigning labels based on closest points and smoothing noise in labels.