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Related lectures (32)

Page 2 of 4

Classification: Decision Trees and kNN

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

Evaluation in NLP

Delves into NLP evaluation, covering gold standards, precision, recall, and statistical significance.

Quantifying Performance: Misclassification and F-Measure

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

Machine Learning in Human Rights: HURIDOCS

Explores machine learning in human rights, focusing on defining goals, handling false positives and negatives, and ensuring transparency and trust.

Machine Learning Basics

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

Information Retrieval Basics: Document Length and Normalization

Explores document length, normalization, bias compensation, and retrieval model evaluation in information retrieval.

Supervised Learning Essentials

Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.

Model Evaluation: K-Nearest Neighbor

Explores model evaluation with K-Nearest Neighbor, covering optimal k selection, similarity metrics, and performance metrics for classification models.

Machine Learning: Features and Model Selection

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

Polynomial Regression: Overview

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