Projects in Digital Humanities Master Program

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (30)

Page 2 of 3

Quantifying Performance: Misclassification and F-Measure

Covers quantifying performance through true positives, false negatives, and false positives in machine learning.

Linear Models for Classification: Logistic Regression and SVM

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

Introduction to Machine Learning

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

Machine Learning for Physicists/Chemists: Image Classification

Covers the fundamentals of machine learning for physicists and chemists, focusing on image classification tasks using artificial intelligence.

Gaussian Naive Bayes & K-NN

Covers Gaussian Naive Bayes, K-nearest neighbors, and hyperparameter tuning in machine learning.

Discrete choice and machine learning: two complementary methodologies

Explores discrete choice and machine learning as complementary methodologies, discussing supervised learning, model advantages, pitfalls, aggregation bias, probabilistic classification, and panel data.

Binary Classification by Regression: Decision Functions and Cost Functions

Explores binary classification by regression, decision functions, and various cost functions.

Support Vector Machines: Interactive Class

Explores Support Vector Machines in machine learning, discussing SVM, support vectors, uniqueness of solutions, and multi-class SVM.

Machine Learning Biases

Explores machine learning basics, adversarial challenges, biases, distributional shift, and deployment complexities.

Generalized Linear Regression

Explores generalized linear regression, logistic regression, and multiclass classification in machine learning.