Projects in Digital Humanities Master Program

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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.

Classification: Decision Trees and kNN

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

Multi-Class Classification: Approaches and Boundaries

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

Gradient Descent: MNIST Dataset and Logistic Loss

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

Classification Algorithms: Generative and Discriminative Approaches

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

Gaussian Discriminant Rule: Classification & Boundaries

Explores the Gaussian Discriminant Rule for classification using Gaussian Mixture Models and discusses drawing boundaries and model complexity.

Logistic Regression: Fundamentals and Applications

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

Machine Learning Fundamentals: Structure Discovery, Classification, Regression

Covers fundamental machine learning concepts including Structure Discovery, Classification, and Regression.

Machine Learning: Features and Model Selection

Delves into the significance of features, model evolution, labeling challenges, and model selection in machine learning.

Linear Models for Classification: Part 3

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