Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.
Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.
Covers random variables, sample spaces, probability distributions, functions, expected value, variance, and estimations.
Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Explores the concept of stationary distribution in Markov chains, discussing its properties and implications, as well as the conditions for positive-recurrence.
Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.
