Eigenvalues and EM Algorithm

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Covers the basics of probability theory, random variables, joint probability, and inference.

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Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.

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Maximum Likelihood Theory & Applications

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

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Explores Gaussian mixture models and denoising noisy signals using a probabilistic approach.

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Covers the normal distribution, including its properties and calculations.

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Discrete Choice Analysis

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Probability and Statistics

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