Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Explores combinatorial mathematics, covering permutations, combinations, and binomial coefficients, along with probability and statistics concepts.
Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
Explores the inclusion-exclusion formula in discrete mathematics, demonstrating its applications through examples and probability scenarios.
Introduces key concepts in probability and statistics, illustrating their application through various examples and emphasizing the importance of mathematical language in understanding the universe.
Covers the basics of probability theory, including definitions, calculations, and important concepts for statistical inference and machine learning.
Introduces key concepts in probability and statistics, such as events, Venn diagrams, and conditional probability.
Introduces key concepts in probability and statistics, covering random experiments, events, intersections, unions, and more.
