Covers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.
Covers the fundamental concepts of probability and statistics, including interesting results, standard model, image processing, probability spaces, and statistical testing.
Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.
Introduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.
Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
