WEEK 9
阿新 • • 發佈:2018-12-03
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8 videos, 1 reading
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6 videos, 1 reading
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.
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.
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
WEEK 9