Machine Learning Basics

Related lectures (31)

Page 3 of 4

Discusses overfitting, model selection, cross-validation, regularization, data representations, and handling imbalanced data in machine learning.

Explores model assessment metrics, selection techniques, bias-variance tradeoff, and handling skewed data distributions in machine learning.

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

Explores generalization, model selection, and validation in machine learning, emphasizing the importance of unbiased model evaluation.

Discusses the experimental framework for selecting and evaluating supervised learning models to prevent overfitting.

Covers logistic regression's probabilistic interpretation, multinomial regression, KNN, hyperparameters, and curse of dimensionality.

Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.

Introduces techniques for obtaining unbiased risk estimates of learned predictors and their application for hyperparameter tuning.

Introduces a course on Machine Learning for Behavioral Data at EPFL, covering ML algorithms, data handling, and model evaluation.