Lectures related to Regularization: Tikhonov and Ridge

Kernel Methods: Understanding Overfitting and Model Selection

Discusses kernel methods, focusing on overfitting, model selection, and kernel functions in machine learning.

Covers the concept of linear regression, including polynomial regression and hyperparameters selection.

Explores linear models, overfitting, and the importance of feature expansion and adding more data to reduce overfitting.

Understanding Data Attributes

Covers the analysis of various data attributes and linear regression models.

Kernel Methods in Machine Learning: Kernel Regression and SVM

Discusses kernel methods in machine learning, focusing on kernel regression and support vector machines, including their formulations and applications.

Regularization in Machine Learning

Introduces regularization techniques to prevent overfitting in machine learning models.

Gradient Descent and Linear Regression

Covers stochastic gradient descent, linear regression, regularization, supervised learning, and the iterative nature of gradient descent.