Linear Classification: Parameterizing Lines and Distance Calculation

Related lectures (31)

Page 2 of 4

Covers supervised learning, binary and multi-class classification problems, and making predictions from labeled examples.

Explains the 0/1 cost function for binary classification and its impact on minimizing prediction errors.

Focuses on implementing gradient descent with the MNIST dataset and logistic loss in machine learning.

Explores neural networks basics, XOR problem, classification, and practical applications like weather data prediction.

Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.

Explores neural networks for regression and classification tasks, covering training, regularization, and practical examples.

Introduces Multi-Layer Perceptrons (MLPs) and covers logistic regression, reformulation, gradient descent, AdaBoost, and practical applications.

Introduces decision trees and k-nearest neighbors for classification tasks, exploring metrics like accuracy and AUC.

Explains how to find the best hyperplane to separate two classes.

Covers the perceptron model and backpropagation algorithm in neural networks.