Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Covers the basics of probability theory, including definitions, calculations, and important concepts for statistical inference and machine learning.
Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.
Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.
Explores conditional probability, the law of total probability, Bayes' theorem, and prediction decomposition.
Covers examples of probability, including Bayes Theorem, independence, and conditional probability.
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.
