Bayesian Inference: Part 2

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

Linear Regression: Estimation and Inference

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Linear Regression: Foundations and Applications

Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.

Supervised Learning Fundamentals

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

Linear Regression: Simple

Introduces simple linear regression, properties of residuals, variance decomposition, and the coefficient of determination in the context of Okun's law.

Linear and Logistic Regression

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

Logistic Regression: Classification

Covers supervised learning, classification using logistic regression, and challenges in optimization.

Overfitting in Supervised Learning: Case Studies and Techniques

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Linear Regression Basics

Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.

Linear Regression Basics

Covers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.