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Explores causal inference, directed graphs, and fairness in algorithms, emphasizing conditional independence and the implications of DAGs.
Covers fundamental concepts in probability and statistics, including the law of total probability, Bayes' theorem, and independence of events.
Covers the basics of probability theory, including definitions, calculations, and important concepts for statistical inference and machine learning.
Delves into quantifying entropy in neuroscience data, exploring how neuron activity represents sensory information and the implications of binary digit sequences.
Covers examples of probability, including Bayes Theorem, independence, and conditional probability.
