Notes-ML-AndrewNg/week9.md

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# 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