Lectures related to Adversarial Training: Theory and Applications

Covers weak solutions in the finite element method, emphasizing continuity and the Cauchy-Schwarz inequality.

Covers inequalities, joint Gaussian distribution, risk estimation, and classification method testing in probability and statistics.

Covers document analysis, topic modeling, and deep generative models, including autoencoders and GANs.

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Explains the use of operators for transferring data between activities in an orchestration graph.

Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.

Covers the estimation of distributions using samples and probability models.

Explores air pollution analysis using wind data, probability distributions, and trajectory models for air quality assessment.

Explores probability theory, distribution of averages, and the central limit theorem.