Implement an anomaly detection algorithm to detect

Question

Programming Exercise: Anomaly Detection and Recommender Systems

Introduction

In this exercise, you will implement the anomaly detection algorithm and apply it to detect failing servers on a network. In the second part, you will use collaborative filtering to build a recommender system for movies. Before starting on the programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics. To get started with the exercise, you will need to download the starter code and unzip its contents to the directory where you wish to complete the exercise. If needed, use the cd command in Octave/MATLAB to change to this directory before starting this exercise.

You can also find instructions for installing Octave/MATLAB in the "Environment Setup Instructions" of the course website.

1 Anomaly detection

In this exercise, you will implement an anomaly detection algorithm to detect anomalous behavior in server computers. The features measure the throughput (mb/s) and latency (ms) of response of each server. While your servers were operating, you collected m = 307 examples of how they were behaving, and thus have an unlabeled dataset x⁽¹⁾, . . . , x^(m). You suspect that the vast majority of these examples are "normal" (non-anomalous) examples of the servers operating normally, but there might also be some examples of servers acting anomalously within this dataset.

2 Recommender Systems

In this part of the exercise, you will implement the collaborative filtering learning algorithm and apply it to a dataset of movie ratings.2 This dataset consists of ratings on a scale of 1 to 5. The dataset has nu = 943 users, and nm = 1682 movies. For this part of the exercise, you will be working with the script ex8 cofi.m.

In the next parts of this exercise, you will implement the function cofiCostFunc.m that computes the collaborative fitlering objective function and gradient. After implementing the cost function and gradient, you will use fmincg.m to learn the parameters for collaborative filtering.

Attachment:- Anomaly Detection and Recommender.rar