Sampling: maximum likelihood estimation

Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.

Covers decoding methods and training challenges in natural language generation.

Covers the solutions to the midterm exam of a Probability Theory course, including calculations of probabilities and expectations.

Introduces the Boltzmann Machine, covering expectation consistency, data clustering, and probability distribution functions.

Covers the inconsistency in estimates due to endogeneity and introduces the BLP and Control-Function estimation methods.