Concept

Naive Bayes spam filtering

Related lectures (13)

Explores cognitive load, metacognition, learning environments, and inductive reasoning, with examples of exam questions and the city of Godthåb.

Spam Filtering: Bayesian Approach

Covers the Bayesian approach to spam filtering and calculating probabilities for identifying spam messages.

Filter Usage

Covers the usage of filters in the EPFL Service Desk environment and managing group assignments.

Statistical Inference and Machine Learning

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

Email Security: Best Practices

Delves into email security statistics, filtering techniques, Cisco Email Security Appliance, authentication, web application access control, vulnerabilities, and antivirus infrastructure migration.

Textual Data Analysis: Classification & Dimensionality Reduction

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

Document Classification: Overview

Explores document classification methods, including k-Nearest-Neighbors, Naïve Bayes Classifier, transformer models, and multi-head attention.

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.

Recommender Systems: Text Classification & Naïve Bayes

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

Probability: Examples

Covers examples of probability, including Bayes Theorem, independence, and conditional probability.

Document Classification

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

Classification Algorithms: Generative and Discriminative Approaches

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