Notes-ML-AndrewNg/week9.md

• 15 Anomaly Detection
• • 15.1 Problem Motivation
  • 15.2 Gaussian Distribution
  • 15.3 Algorithm
  • 15.4 Developing and Evaluating an Anomaly Detection System
  • 15.5 Anomaly Detection vs. Supervised Learning
  • 15.6 Choosing What Features to Use
  • 15.7 Multivariate Gaussian Distribution
  • 15.8 Anomaly Detection using the Multivariate Gaussian Distribution
• 16 Recommender Systems
• • 16.1 Problem Formulation
  • 16.2 Content Based Recommendations
  • 16.3 Collaborative Filtering
  • 16.4 Collaborative Filtering Algorithm
  • 16.5 Vectorization_ Low Rank Matrix Factorization
  • 16.6 Implementational Detail_ Mean Normalization

15 Anomaly Detection

15.1 Problem Motivation

15.2 Gaussian Distribution

15.3 Algorithm

15.4 Developing and Evaluating an Anomaly Detection System

15.5 Anomaly Detection vs. Supervised Learning

15.6 Choosing What Features to Use

15.7 Multivariate Gaussian Distribution

15.8 Anomaly Detection using the Multivariate Gaussian Distribution

16 Recommender Systems

16.1 Problem Formulation

16.2 Content Based Recommendations

16.3 Collaborative Filtering

16.4 Collaborative Filtering Algorithm

16.5 Vectorization_ Low Rank Matrix Factorization

16.6 Implementational Detail_ Mean Normalization