Loss functions for classification

Applied sciences
Information engineering
Machine learning
Decision tree learning

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (28)

Neural Networks: Training and Activation

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Building Robust Ensembles via Margin Boosting

Delves into building robust ensembles through margin boosting for improved adversarial defense in machine learning models.

Classification Problems: Overview and Loss Functions

Covers classification problems and various loss functions used in machine learning.

Exploration Bias

Explores regularization, learning algorithms, and subgaussian assumptions in machine learning.

Untitled

Supervised Learning: Linear Regression

Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.

Generalized Linear Regression

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

Boosting: Adaboost Algorithm

Covers boosting with a focus on the Adaboost algorithm, forward stagewise additive modeling, and gradient tree boosting.

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

Bayesian Inference: Part 2

Explores Bayesian inference, multiclass classification, logistic regression, and linear regression inference.

Gradient Descent: Early Stopping and Stochastic Gradient Descent

Explains gradient descent with early stopping and stochastic gradient descent to optimize model training and prevent overfitting.

Logistic Regression: New Formulation and Loss Function

Explores a new formulation of logistic regression and its connection to empirical risk minimization.

Linear Binary Classification

Covers the extension of the 0-1 loss to real-valued score functions and logistic regression.

Binary Classification by Regression: Decision Functions and Cost Functions

Explores binary classification by regression, decision functions, and various cost functions.

Gradient Descent: Optimization Techniques

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

Gradient Descent: Linear Regression

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

Maximum Likelihood Estimation: Linear Models

Explores maximum likelihood estimation in linear models, covering Gaussian noise, covariance estimation, and support vector machines for classification problems.

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.

SVMs and Feature Maps

Explores SVMs, feature maps, and the importance of finding the maximum margin solution for classification problems.

Uniform convergence and No-Free-Lunch theorem

Explores uniform convergence, loss functions, and the No-Free-Lunch theorem in machine learning.

Page 1 of 2