Causal Inference & Directed GraphsExplores causal inference, directed graphs, and fairness in algorithms, emphasizing conditional independence and the implications of DAGs.
Probability and StatisticsCovers fundamental concepts in probability and statistics, including the law of total probability, Bayes' theorem, and independence of events.
Probability: IndependenceExplores the concept of independence in probability theory, showing how events can occur without influencing each other.
Lovász Local Lemma: BasicsCovers the basics of the Lovász Local Lemma, including mutually independent bad events and pseudoprobabilities.
Stochastic Models for CommunicationsCovers random vectors, joint probability density, independent random variables, functions of two random variables, and Gaussian random variables.
Introduction to Probability TheoryCovers the basics of probability theory, including definitions, calculations, and important concepts for statistical inference and machine learning.
