The Application of Anomaly Detection in Machine Learning

Try a walking desk to stay healthy while you study or work!

Full notes at ocdevel.com/mlg/14

• Applications: Credit card fraud detection and server activity monitoring.
• Concept: Identifying outliers on a bell curve.
• Statistics: Central role of the Gaussian distribution (normal distribution) in detecting anomalies.
• Process: Identifying significant deviations from the mean to detect outliers.

• Types:
  • Content Filtering: Uses features of items (e.g., Pandora’s Music Genome Project).
  • Collaborative Filtering: Based on user behavior and preferences, like "Users Also Liked" model utilized in platforms like Netflix and Amazon.
• Applications in Machine Learning: Linear regression applications in recommender systems for predicting user preferences.

• Explanation: Series of states with probabilities dictating transitions to next states; present state is sufficient for predicting next state (Markov principle).
• Use Cases: Often found in reinforcement learning and operations research.
• Monte Carlo Simulation: Running simulations to determine the expected value or probable outcomes of Markov processes.

• Andrew NG's Coursera Course - Week 9: Focuses on anomaly detection and recommender systems.