Document Classification: Overview

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Related lectures (29)

Page 1 of 3

Nearest Neighbor Rules: Part 2

Explores the Nearest Neighbor Rules, k-NN algorithm challenges, Bayes classifier, and k-means algorithm for clustering.

Naive Bayes Classifier

Introduces the Naive Bayes classifier, covering independence assumptions, conditional probabilities, and applications in document classification and medical diagnosis.

Document Classification: Features and Models

Introduces document classification using features like words and metadata, and models such as k-Nearest-Neighbors and word embeddings.

Statistical Inference and Machine Learning

Covers statistical inference, machine learning, SVMs for spam classification, email preprocessing, and feature extraction.

Supervised Learning: Classification Algorithms

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.

Regularized Cross-Entropy Risk

Explores the regularized cross-entropy risk in neural networks, covering training processes and challenges in deep networks.

Neural Networks: Multilayer Perceptrons

Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.

Document Classification

Explores document classification methods, including Naïve Bayes and word embeddings.

Textual Data Analysis: Classification & Dimensionality Reduction

Explores textual data classification, focusing on methods like Naive Bayes and dimensionality reduction techniques like Principal Component Analysis.

Recommender Systems: Text Classification & Naïve Bayes

Explores text classification using Naïve Bayes in content-based recommenders.