Lectures related to Untitled | EPFL Graph Search

Introduction to Machine Learning: Linear Models

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

Kernel Methods: Machine Learning

Covers Kernel Methods in Machine Learning, focusing on overfitting, model selection, cross-validation, regularization, kernel functions, and SVM.

Introduction to Machine Learning: Supervised Learning

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Kernel Methods: Machine Learning

Explores kernel methods in machine learning, emphasizing their application in regression tasks and the prevention of overfitting.

Nonparametric Regression

Covers nonparametric regression, scatterplot smoothing, kernel methods, and bias-variance tradeoff.

Kernel Methods: Neural Networks

Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.

Linear Models: Recap and Extensions

Covers linear models, multi-class classification, k-Nearest Neighbors, and feature expansion techniques.

Kernel Methods and Regression

Covers kernel methods, kernel regression, RBF kernel, and SVM for classification.

Kernel Methods

Covers overfitting, model selection, validation methods, kernel functions, and SVM concepts.

K-Nearest Neighbors & Feature Expansion

Introduces k-Nearest Neighbors for classification and feature expansion to handle nonlinear data through transformed inputs.

K-Nearest Neighbors & Feature Expansion

Introduces k-Nearest Neighbors method and feature expansion for nonlinear machine learning through polynomial transformations.

Machine Learning Fundamentals

Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.

Feature Expansion and Kernel Methods Log in to Mediaspace to watch this video

Explores feature expansion, kernel methods, SVM, and nonlinear classification in machine learning.

Kernel Methods: SVM and Regression Log in to Mediaspace to watch this video

Introduces kernel methods like SVM and regression, covering concepts such as margin, support vector machine, curse of dimensionality, and Gaussian process regression.

Kernel Regression: K-nearest Neighbors Log in to Mediaspace to watch this video

Covers the concept of kernel regression and K-nearest neighbors for making data linearly separable.

Nonlinear Machine Learning: k-Nearest Neighbors and Feature Expansion Log in to Mediaspace to watch this video

Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.

Kernel Regression: Basics and Applications Log in to Mediaspace to watch this video

Explores kernel regression, the curse of dimensionality, and random features in neural networks.

Feature Expansion and Kernels Log in to Mediaspace to watch this video

Covers feature expansion, kernels, SVM, and nonlinear classification in machine learning.

Machine Learning Fundamentals: Regularization and Cross-validation Log in to Mediaspace to watch this video

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

Machine Learning Fundamentals Log in to Mediaspace to watch this video

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.