Maximum Likelihood Estimation: Linear Models

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Introduces the basics of probability models, covering random variables, distributions, and statistical estimation.

Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

Explores regularization in linear models, including Ridge Regression and the Lasso, analytical solutions, and polynomial ridge regression.

Explores generalized linear regression, logistic regression, and multiclass classification in machine learning.

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Covers the Mathematics of Data, focusing on models, estimators, and practical issues in data analysis.

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

Covers the likelihood ratio test in choice models, benchmarking, and tests for taste variations and nonlinear specifications.