Mathematics of Data: Data, Models, and Optimization

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Explores logistic regression for binary classification, covering probability modeling, optimization methods, and regularization techniques.

Covers the fundamental concepts of statistics and their application in data science.

Explores Bayesian estimation for unsupervised learning and MCMC, using a Spin Glass Card game example.

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Covers the Bayes discriminant rule for allocating individuals to populations based on measurements and prior probabilities.

Explores the Cramér-Rao bound, hypothesis testing, and optimality in statistical theory.

Explores optimality in decision theory and unbiased estimation, emphasizing sufficiency, completeness, and lower bounds for risk.

Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.