Scott's world.

Week-9

Word count: 229Reading time: 1 min
2019/07/27 Share

Week 9

Density Estimation(异常检测)

Problem Motivation

  • Density Estimation Algotithm

  • Anomaly detection example

Gaussian Distribution(高斯分布或正态分布)

The formula for the Gaussian density is:

  • Gaussian distribution example

  • Parameter estimation

Algorithm

  • Anomaly detection example

Building an Anomaly Detection System

Developing and Evaluating an Anomaly Detection System

  • the important of real-number evaluation

  • Aircraft engines motivating example

  • Alogorithm evaluation

Anomaly Detection vs. Supervised Learning

Choosing What Features to Use

  • Non-gaussian features

  • Error analysis for anomaly detection

  • Monitoring computers in a data center

Multivariate Gaussian Distribution(Optional)

Multivariate Gaussian Distribution

  • Motivating example:Monitoring machines in a data center

  • Multivariate Gaussian(Normal)distribution

  • Multivariate Gaussian(Normal) examples

Anomaly Detection using the Multivariate Gaussian Distribution

  • Multivariate Gaussian (Normal) distribution

  • Anomaly detection with the multivariate Gaussian

  • Relationship to original model

  • Original model vs. Multivariate Gaussian

Predicting Movie Ratings

Problem Formulation

  • Example : Predicting movie ratings

Content Based Recommendations

  • Content-based recommender systems

  • Problem formulation

  • Optimization objective

  • Optimization algorithm

Collaborative Filtering

Collaborative Filtering

  • Problem motivation

  • Optimization algorithm

  • Collaborative filtering

Collaborative Filtering Algorithm

  • Collaborative filtering optimization objective

  • Collaborative filtering algorithm

Low Rank Matrix Factorization(低秩矩阵分解)

Vectorization: Low Rank Matrix Factorization

  • Collaborative filtering

  • Finding related movies

Implementational Detail: Mean Normalization

  • Users who have not reated any movies

  • Mean Normalizatio

CATALOG
  1. 1. Week 9
    1. 1.1. Density Estimation(异常检测)
      1. 1.1.1. Problem Motivation
      2. 1.1.2. Gaussian Distribution(高斯分布或正态分布)
      3. 1.1.3. Algorithm
    2. 1.2. Building an Anomaly Detection System
      1. 1.2.1. Developing and Evaluating an Anomaly Detection System
      2. 1.2.2. Anomaly Detection vs. Supervised Learning
      3. 1.2.3. Choosing What Features to Use
    3. 1.3. Multivariate Gaussian Distribution(Optional)
      1. 1.3.1. Multivariate Gaussian Distribution
      2. 1.3.2. Anomaly Detection using the Multivariate Gaussian Distribution
    4. 1.4. Predicting Movie Ratings
      1. 1.4.1. Problem Formulation
      2. 1.4.2. Content Based Recommendations
    5. 1.5. Collaborative Filtering
      1. 1.5.1. Collaborative Filtering
      2. 1.5.2. Collaborative Filtering Algorithm
    6. 1.6. Low Rank Matrix Factorization(低秩矩阵分解)
      1. 1.6.1. Vectorization: Low Rank Matrix Factorization
      2. 1.6.2. Implementational Detail: Mean Normalization