MLG 014 Shallow Algos 3

Apr 23, 2017
Click to Play Episode

Anomaly Detection, Recommenders (Content Filtering vs Collaborative Filtering), and Markov Chain Monte Carlo (MCMC)

Resources
Resources best viewed here
Loading...
Show Notes
CTA

Sitting for hours drains energy and focus. A walking desk boosts alertness, helping you retain complex ML topics more effectively.Boost focus and energy to learn faster and retain more.Discover the benefitsDiscover the benefits

Anomaly Detection Systems

  • 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.

Recommender Systems

  • 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.

Markov Chains

  • 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.

Resource