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Related lectures (31)

Gradient Descent Methods: Theory and Computation

Explores gradient descent methods for smooth convex and non-convex problems, covering iterative strategies, convergence rates, and challenges in optimization.

Elements of Statistics: Probability, Distributions, and Estimation

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

Optimality and Asymptotics

Explores the optimality of the Least Squares Estimator and its large sample distribution.

Distribution Theory of Least Squares

Explores the distribution theory of least squares estimators in a Gaussian linear model.

Weighted Least Squares Estimation: IRLS Algorithm

Explores the IRLS algorithm for weighted least squares estimation in GLM.

Extreme Values Detection

Covers methods for identifying and handling extreme values in data, including statistical procedures for outlier detection.

Distribution Theory of Least Squares

Explores the distribution theory of least squares estimators in a Gaussian linear model, focusing on precision and confidence intervals construction.

Statistics for Data Science: Introduction to Statistical Methods

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

Parametric Models: Estimation and Optimization

Explores parametric models, estimation techniques, regression models, and score-based classifiers in data analysis.

Distribution Estimation

Explores distribution estimation, constraints in estimators, and competitive analysis for robust estimation.

Logistic Regression: Maximum Likelihood Estimation

Covers logistic regression and maximum likelihood estimation in depth.

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