Parametric Models: Mathematics of Data

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Related lectures (32)

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Basics of Linear Regression

Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.

Supervised Learning Fundamentals

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

Supervised Learning: Likelihood Maximization

Covers supervised learning through likelihood maximization to find optimal parameters.

Model Selection Criteria: AIC, BIC, Cp

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

Generalized Linear Regression: Classification

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Weighted Least Squares Estimation: IRLS Algorithm

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

Supervised Learning Essentials

Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.

Linear Regression Basics

Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.

Linear and Logistic Regression

Covers linear and logistic regression, including underfitting, overfitting, and performance metrics.

Cross-Validation: Techniques and Applications

Explores cross-validation, overfitting, regularization, and regression techniques in machine learning.