WEEK 9

Posted 2021-01-24 keyshaw

WEEK 9

Anomaly Detection

Given a large number of data points, we may sometimes want to figure out which ones vary significantly from the average. For example, in manufacturing, we may want to detect defects or anomalies. We show how a dataset can be modeled using a Gaussian distribution, and how the model can be used for anomaly detection.
8 videos, 1 reading

Video: Problem Motivation

Video: Gaussian Distribution

Video: Algorithm

Video: Developing and Evaluating an Anomaly Detection System

Video: Anomaly Detection vs. Supervised Learning

Video: Choosing What Features to Use

Video: Multivariate Gaussian Distribution

Video: Anomaly Detection using the Multivariate Gaussian Distribution

Reading: Lecture Slides

Graded: Anomaly Detection

Recommender Systems

When you buy a product online, most websites automatically recommend other products that you may like. Recommender systems look at patterns of activities between different users and different products to produce these recommendations. In this module, we introduce recommender algorithms such as the collaborative filtering algorithm and low-rank matrix factorization.
More
6 videos, 1 reading

Video: Problem Formulation

Video: Content Based Recommendations

Video: Collaborative Filtering

Video: Collaborative Filtering Algorithm

Video: Vectorization: Low Rank Matrix Factorization

Video: Implementational Detail: Mean Normalization

Reading: Lecture Slides

Programming: Anomaly Detection and Recommender Systems

Graded: Recommender Systems