Learning

Probabilistic machine learning, mixture models, and approximate Bayesian inference.

Foundations of NN, CNNs, LSTMs, GANs, Transformers, transfer learning, and autoencoders.

Quantitative social science: data analysis, research design, probability, inference.

Linear regression models and extensions.