Elementary probability: Theory and calculations

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Covers fundamental concepts of probability and statistics, focusing on data analysis, graphical representation, and practical applications.

Covers graphical models for probability distributions and factor graphs representation.

Explores risk analysis in project management, covering identification, prevention, quantification, and lean management.

Explains joint random variables, marginal laws, and independence implications.

Discusses probability distributions and the Central Limit Theorem, emphasizing their importance in data science and statistical analysis.

Covers probabilistic methods in combinatorics, monochromatic edges, 2-colorable graphs, and good 2-colorings.

Explores reversibility in Markov chains and its impact on the stationary distribution, highlighting the complexity of non-reversible chains.

Discusses quantum science exercises on photoemission, interference, and particle behavior, emphasizing student engagement and problem-solving.

Covers the Poisson Paradigm, including the First/Second Moment Method and Martingales, discussing dependency graphs and Chernoff bounds.

Introduces statistics basics, including data analysis and probability theory, emphasizing central tendency, dispersion, and distribution shapes.

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