Bayesian Networks: Fundamentals and Applications

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Text Models: Word Embeddings and Topic Models

Explores word embeddings, topic models, Word2vec, Bayesian Networks, and inference methods like Gibbs sampling.

Probability and Statistics

Covers probability, statistics, independence, covariance, correlation, and random variables.

Supervised Learning Fundamentals

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Copulas and Tail Dependence

Explores copulas, rank correlations, and tail dependence measures in risk management.

Deep Generative Models

Covers deep generative models, including LDA, autoencoders, GANs, and DCGANs.

Continuous Random Variables

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.

Variational Inference and Neural Networks

Covers variational inference and neural networks for classification tasks.

Word Embeddings: Models and Learning

Explores word embeddings, context importance, and learning algorithms for creating new representations.

Natural Language Processing

Introduces Natural Language Processing, covering text preprocessing, sentiment analysis, and topic analysis, with a focus on building a climate change risk index.

Probability and Statistics

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