Linear Regression and Gradient Descent

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Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Linear and Ridge Regression

Covers linear and ridge regression, overfitting, hyperparameters, and test sets.

Nonlinear ML Algorithms

Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.

Overfitting in Supervised Learning: Case Studies and Techniques

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

Supervised Learning in Financial Econometrics

Explores supervised learning in financial econometrics, covering linear regression, model fitting, potential problems, basis functions, subset selection, cross-validation, regularization, and random forests.

Polynomial Regression: Basics and Regularization

Covers the basics of polynomial regression and regularization to prevent overfitting.

Linear Regression and Logistic Regression

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

Cross-Validation: Techniques and Applications

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

Linear Regression: Statistical Inference and Regularization

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

Overfitting, Cross-validation, Regularization

Explores overfitting, cross-validation, and regularization in machine learning, emphasizing model complexity and the importance of regularization strength.