Concept

Anti-spam techniques

Related lectures (13)

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

Spam Filtering: Bayesian Approach

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

Learning Sciences Concepts

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

Statistical Inference and Machine Learning

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

Building Usenet: Specifications and Message Format

Explores the history, specifications, and limitations of Usenet, emphasizing message format and broadcast efficiency.

Supervised Learning: Linear Regression

Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.

Logistic Regression: Interpretation & Feature Engineering

Covers logistic regression, probabilistic interpretation, and feature engineering techniques.

Unsupervised Learning: PCA & K-means

Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.

Probability: Examples

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

Phishing: Recognizing Email Scams

Covers how to recognize email scams, including impersonal signatures and urgent messages.

Digital Image Correlation: Practical

Focuses on the practical application of Digital Image Correlation for civil engineers, covering measuring displacement fields and computing strain fields.

Evolution of Usenet: From ARPANet to Commercial Spam

Explores the evolution of Usenet from ARPANet to commercial spam in the mid-1990s.

Link-Based Ranking: Introduction

Explores the significance of anchor text in link-based ranking and its impact on search results.