Lectures related to Untitled | EPFL Graph Search

Explores generalized linear regression, logistic regression, and multiclass classification in machine learning.

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

Gradient Descent: Linear Regression

Covers the concept of gradient descent for linear regression, explaining the iterative process of updating parameters.

Linear Regression: Statistical Inference and Regularization

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

Cross-Validation: Techniques and Applications

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

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.

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.

Cross-validation & Regularization

Explores polynomial curve fitting, kernel functions, and regularization techniques, emphasizing the importance of model complexity and overfitting.

Overfitting in Supervised Learning: Case Studies and Techniques

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

Nonlinear ML Algorithms

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

Optimization in Machine Learning: Gradient Descent

Covers optimization in machine learning, focusing on gradient descent for linear and logistic regression, stochastic gradient descent, and practical considerations.

Regularization in Machine Learning

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

Gradient Descent: Optimization Techniques

Explores gradient descent, loss functions, and optimization techniques in neural network training.

Optimization Techniques: Gradient Method Overview

Discusses the gradient method for optimization, focusing on its application in machine learning and the conditions for convergence.

Overfitting, Cross-validation & Regularization

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

Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Supervised Learning: Regression Methods

Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.

Polynomial Regression: Basics and Regularization

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

Deep Learning: Designing Neural Network Models

Covers the design and optimization of neural network models in deep learning.

Multilayer Neural Networks: Deep Learning

Covers the fundamentals of multilayer neural networks and deep learning.