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Related lectures (30)

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Linear Models for Classification: Multi-Class Extensions

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Linear Classification: Logistic Regression

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

Linear Models for Classification: Part 3

Explores linear models for classification, including binary classification, logistic regression, decision boundaries, and support vector machines.

Logistic Regression: Interpretation & Feature Engineering

Covers logistic regression, probabilistic interpretation, and feature engineering techniques.

Linear Models: Classification

Explores linear models for classification, including logistic regression, decision boundaries, and support vector machines.

Neural Networks: Multilayer Learning

Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.

Introduction to Machine Learning

Covers the basics of machine learning for physicists and chemists, focusing on image classification and dataset labeling.

Support Vector Machine and Logistic Regression

Explains support vector machine and logistic regression for classification tasks, emphasizing margin maximization and risk minimization.

Linear Models for Classification

Explores linear models for classification, logistic regression, decision boundaries, SVM, multi-class classification, and practical applications.

Generalized Linear Regression: Classification

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