Classification Problems: Overview and Loss Functions

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (32)

Linear Models for Classification: Logistic Regression and SVM Log in to Mediaspace to watch this video

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

Supervised Learning: Classification Algorithms Log in to Mediaspace to watch this video

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.

Classification Algorithms: Generative and Discriminative Approaches Log in to Mediaspace to watch this video

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

Logistic Regression: Fundamentals and Applications Log in to Mediaspace to watch this video

Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.

Decision Trees: Classification Log in to Mediaspace to watch this video

Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.

Linear Classification: Logistic Regression Log in to Mediaspace to watch this video

Covers linear classification using logistic regression, regularization, and multiclass classification.

Linear and Logistic Regression Log in to Mediaspace to watch this video

Introduces linear and logistic regression, covering parametric models, multi-output prediction, non-linearity, gradient descent, and classification applications.

Supervised Learning Essentials Log in to Mediaspace to watch this video

Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.

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.

Link Prediction: Missing Edges and Probabilistic Methods Log in to Mediaspace to watch this video

Explores link prediction in networks, covering missing edges, probabilistic methods, and causal inference challenges.

Classification with GMM and kNN Log in to Mediaspace to watch this video

Covers classification using GMM and kNN, exploring boundaries, errors, and practical exercises.

Linear and Weighted Regression: Optimal Parameters and Local Solutions Log in to Mediaspace to watch this video

Covers linear and weighted regression, optimal parameters, local solutions, SVR application, and regression techniques' sensitivity.

Page 2 of 2