Projects in Digital Humanities Master Program

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

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

Explains the strategies for multi-class classification and the concept of decision boundaries.

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

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