Likelihood Estimation and Least Squares

Related lectures (30)

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Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

Introduces linear models, regression, Gaussian distribution, linearity, and model generalization.

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Explores linear regression, least squares, residuals, and confidence intervals in regression models.

Explores linear regression estimation, hypothesis testing, and practical applications in statistics.

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

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.

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

Explores non-parametric statistics, Bayesian methods, and linear regression with a focus on kernel density estimation and posterior distribution.

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.