Machine Learning in Human Rights: HURIDOCS

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Related lectures (32)

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Linear Regression: Basics

Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.

Machine Learning Basics

Introduces machine learning basics, including data collection, model evaluation, and feature normalization.

Linear Models for Classification: Multi-Class Extensions

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

Performance Evaluation: Bootstrap and Performance Metrics

Explores machine learning model evaluation using leave-one-out, bootstrap, and performance metrics like recall and precision.

Performance Criteria: Confusion Matrix, Recall, Precision, Accuracy

Explores performance criteria in supervised learning, emphasizing precision, recall, and specificity in model evaluation.

Understanding ImageNet Classifiers

Explores the generalization of ImageNet classifiers and the challenges in achieving reliable machine learning models.

Machine learning: Basics of data-driven materials modeling

Covers dimensionality reduction and linear regression in data-driven materials modeling.

Polynomial Regression: Overview

Covers polynomial regression, flexibility impact, and underfitting vs overfitting.

Logistic Regression: Fundamentals and Applications

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

Transformers: Unifying Machine Learning Communities

Covers the role of Transformers in unifying various machine learning fields.