Proximal Operators: Optimization Methods

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Related lectures (28)

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Linear Models for Classification: Logistic Regression and SVM

Covers linear models for classification, focusing on logistic regression and support vector machines.

Machine Learning Fundamentals: Regularization and Cross-validation

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

Linear Models and Overfitting

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

Linear Models: Recap and Logistic Regression

Covers linear models, binary classification, logistic regression, and model evaluation metrics.

Ridge Regression: Penalised Least Squares

Explores Ridge Regression for handling multicollinearity and the LASSO method for model selection.

Linear Models: Continued

Explores linear models, regression, multi-output prediction, classification, non-linearity, and gradient-based optimization.

Regularization in Machine Learning

Explores Ridge and Lasso Regression for regularization in machine learning models, emphasizing hyperparameter tuning and visualization of parameter coefficients.

Logistic Regression: Cost Functions & Optimization

Explores logistic regression, cost functions, gradient descent, and probability modeling using the logistic sigmoid function.

Linear Regression and Logistic Regression

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

Gradient Descent and Linear Regression

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