Causal Inference & Directed GraphsExplores causal inference, directed graphs, and fairness in algorithms, emphasizing conditional independence and the implications of DAGs.
Continuous Random VariablesExplores continuous random variables, density functions, joint variables, independence, and conditional densities.
Probabilistic Linear RegressionExplores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
DFS Continuation: Topological SortCovers topics like DFS output, edge classification, acyclic graphs, correctness, time analysis, SCCs, and the Topological Sort algorithm.
Probability and StatisticsCovers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.
Multinomial DistributionCovers the multinomial distribution, joint density, marginal distribution, and conditional distribution.
