In this article, you will find **Coursera machine learning week 9 assignment answers – Andrew Ng. ** Use “Ctrl+F” To Find Any Questions or Answers. For Mobile Users, You Just Need To Click On Three dots In Your Browser & You Will Get A “Find” Option There. Use These Options to Get Any Random Questions Answer.

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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.

### Coursera machine learning week 9 assignment answers

function [mu sigma2] = estimateGaussian(X) %ESTIMATEGAUSSIAN This function estimates the parameters of a %Gaussian distribution using the data in X % [mu sigma2] = estimateGaussian(X), % The input X is the dataset with each n-dimensional data point in one row % The output is an n-dimensional vector mu, the mean of the data set % and the variances sigma^2, an n x 1 vector % % Useful variables [m, n] = size(X); % You should return these values correctly mu = zeros(n, 1); sigma2 = zeros(n, 1); % ====================== YOUR CODE HERE ====================== % Instructions: Compute the mean of the data and the variances % In particular, mu(i) should contain the mean of % the data for the i-th feature and sigma2(i) % should contain variance of the i-th feature. % mu = ((1/m)*sum(X))'; sigma2 = ((1/m)*sum((X-mu').^2))'; % ============================================================= end

function [bestEpsilon bestF1] = selectThreshold(yval, pval) %SELECTTHRESHOLD Find the best threshold (epsilon) to use for selecting %outliers % [bestEpsilon bestF1] = SELECTTHRESHOLD(yval, pval) finds the best % threshold to use for selecting outliers based on the results from a % validation set (pval) and the ground truth (yval). % bestEpsilon = 0; bestF1 = 0; F1 = 0; stepsize = (max(pval) - min(pval)) / 1000; for epsilon = min(pval):stepsize:max(pval) % ====================== YOUR CODE HERE ====================== % Instructions: Compute the F1 score of choosing epsilon as the % threshold and place the value in F1. The code at the % end of the loop will compare the F1 score for this % choice of epsilon and set it to be the best epsilon if % it is better than the current choice of epsilon. % % Note: You can use predictions = (pval < epsilon) to get a binary vector % of 0's and 1's of the outlier predictions cvPredictions = (pval < epsilon); % m x 1 tp = sum((cvPredictions == 1) & (yval == 1)); % m x 1 fp = sum((cvPredictions == 1) & (yval == 0)); % m x 1 fn = sum((cvPredictions == 0) & (yval == 1)); % m x 1 prec = tp/(tp+fp); rec = tp/(tp+fn); F1 = 2*prec*rec / (prec + rec); % ============================================================= if F1 > bestF1 bestF1 = F1; bestEpsilon = epsilon; end end end

function [J, grad] = cofiCostFunc(params, Y, R, num_users, num_movies, ... num_features, lambda) %COFICOSTFUNC Collaborative filtering cost function % [J, grad] = COFICOSTFUNC(params, Y, R, num_users, num_movies, ... % num_features, lambda) returns the cost and gradient for the % collaborative filtering problem. % % Unfold the U and W matrices from params X = reshape(params(1:num_movies*num_features), num_movies, num_features); Theta = reshape(params(num_movies*num_features+1:end), ... num_users, num_features); % You need to return the following values correctly J = 0; X_grad = zeros(size(X)); % Nm x n Theta_grad = zeros(size(Theta)); % Nu x n % ====================== YOUR CODE HERE ====================== % Instructions: Compute the cost function and gradient for collaborative % filtering. Concretely, you should first implement the cost % function (without regularization) and make sure it is % matches our costs. After that, you should implement the % gradient and use the checkCostFunction routine to check % that the gradient is correct. Finally, you should implement % regularization. % % Notes: X - num_movies x num_features matrix of movie features % Theta - num_users x num_features matrix of user features % Y - num_movies x num_users matrix of user ratings of movies % R - num_movies x num_users matrix, where R(i, j) = 1 if the % i-th movie was rated by the j-th user % % You should set the following variables correctly: % % X_grad - num_movies x num_features matrix, containing the % partial derivatives w.r.t. to each element of X % Theta_grad - num_users x num_features matrix, containing the % partial derivatives w.r.t. to each element of Theta % %% %%%%% WORKING: Without Regularization %%%%%%%%%% Error = (X*Theta') - Y; J = (1/2)*sum(sum(Error.^2.*R)); X_grad = (Error.*R)*Theta; % Nm x n Theta_grad = (Error.*R)'*X; % Nu x n %% %%%%% WORKING: With Regularization Reg_term_theta = (lambda/2)*sum(sum(Theta.^2)); Reg_term_x = (lambda/2)*sum(sum(X.^2)); J = J + Reg_term_theta + Reg_term_x; X_grad = X_grad + lambda*X; % Nm x n Theta_grad = Theta_grad + lambda*Theta; % Nu x n % ============================================================= grad = [X_grad(:); Theta_grad(:)]; end

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