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All of Probability: LLN, CLT, Chernoff and PAC bound
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Related lectures (28)
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Statistics: Exploratory Data Analysis
Introduces statistics basics, including data analysis and probability theory, emphasizing central tendency, dispersion, and distribution shapes.
Normal Distribution: Properties and Calculations
Covers the normal distribution, including its properties and calculations.
All of Probability: Basic Bounds, LLN & CLT
Introduces basic bounds, LLN, and CLT in probability theory, emphasizing convergence to normal distribution.
Probability and Statistics
Explores joint random variables, conditional density, and independence in probability and statistics.
Probability and Statistics
Introduces key concepts in probability and statistics, such as events, Venn diagrams, and conditional probability.
Conditional Probability: Prediction Decomposition
Explores conditional probability, Bayes' theorem, and prediction decomposition for informed decision-making.
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Introduces key concepts in probability and statistics, illustrating their application through various examples and emphasizing the importance of mathematical language in understanding the universe.
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Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.
Martingales: More Theory
Explores the theory of martingales, including conditional expectations, Chernoff bounds, and Azuma's inequality.
Probability and Statistics
Introduces key concepts in probability and statistics, covering random experiments, events, intersections, unions, and more.
