Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.
Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.
Covers the fundamentals of Bayesian estimation, focusing on the application of Bayes' Theorem in scalar estimation.
Covers the basics of probability theory, including definitions, calculations, and important concepts for statistical inference and machine learning.
