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 “En- vironment Setup Instructions” of the course website.
Files included in this exercise
ex8.m - Octave/MATLAB script for first part of exercise
ex8 cofi.m - Octave/MATLAB script for second part of exercise ex8data1.mat - First example Dataset for anomaly detection ex8data2.mat - Second example Dataset for anomaly detection ex8 movies.mat - Movie Review Dataset
ex8 movieParams.mat - Parameters provided for debugging multivariateGaussian.m - Computes the probability density function for a Gaussian distribution
visualizeFit.m - 2D plot of a Gaussian distribution and a dataset checkCostFunction.m - Gradient checking for collaborative filtering computeNumericalGradient.m - Numerically compute gradients
fmincg.m - Function minimization routine (similar to fminunc) loadMovieList.m - Loads the list of movies into a cell-array movie ids.txt - List of movies
normalizeRatings.m - Mean normalization for collaborative filtering submit.m - Submission script that sends your solutions to our servers [?] estimateGaussian.m - Estimate the parameters of a Gaussian dis- tribution with a diagonal covariance matrix
[?] selectThreshold.m - Find a threshold for anomaly detection
[?] cofiCostFunc.m - Implement the cost function for collaborative fil- tering
? indicates files you will need to complete
Throughout the first part of the exercise (anomaly detection) you will be using the script ex8.m. For the second part of collaborative filtering, you will use ex8 cofi.m. These scripts set up the dataset for the problems and make calls to functions that you will write. You are only required to modify functions in other files, by following the instructions in this assignment.
Submission and Grading
After completing various parts of the assignment, be sure to use the submit function system to submit your solutions to our servers. The following is a breakdown of how each part of this exercise is scored.
Part
Submitted File
Points
Estimate Gaussian Parameters
Select Threshold
estimateGuassian.m
selectThreshold.m
15 points
15 points
Collaborative Filtering Cost
Collaborative Filtering Gradient
Regularized Cost
Gradient with regularization
cofiCostFunc.m
cofiCostFunc.m cofiCostFunc.m cofiCostFunc.m
20 points
30 points
10 points
10 points
Total Points
100 points
You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.
