diff --git a/Programming Assignment/Week_4/Chirag_Week4/computeCentroids.m b/Programming Assignment/Week_4/Chirag_Week4/computeCentroids.m
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+++ b/Programming Assignment/Week_4/Chirag_Week4/computeCentroids.m	
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+function centroids = computeCentroids(X, idx, K)
+%COMPUTECENTROIDS returns the new centroids by computing the means of the 
+%data points assigned to each centroid.
+%   centroids = COMPUTECENTROIDS(X, idx, K) returns the new centroids by 
+%   computing the means of the data points assigned to each centroid. It is
+%   given a dataset X where each row is a single data point, a vector
+%   idx of centroid assignments (i.e. each entry in range [1..K]) for each
+%   example, and K, the number of centroids. You should return a matrix
+%   centroids, where each row of centroids is the mean of the data points
+%   assigned to it.
+%
+
+% Useful variables
+[m n] = size(X);
+
+% You need to return the following variables correctly.
+centroids = zeros(K, n);
+
+
+% ====================== YOUR CODE HERE ======================
+% Instructions: Go over every centroid and compute mean of all points that
+%               belong to it. Concretely, the row vector centroids(i, :)
+%               should contain the mean of the data points assigned to
+%               centroid i.
+%
+% Note: You can use a for-loop over the centroids to compute this.
+%
+
+for i = 1:K,
+  centroids(i,:) = sum((X.*(idx==i)))/sum(idx==i);
+endfor
+
+
+
+
+
+
+% =============================================================
+
+
+end
+
diff --git a/Programming Assignment/Week_4/Chirag_Week4/findClosestCentroids.m b/Programming Assignment/Week_4/Chirag_Week4/findClosestCentroids.m
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+++ b/Programming Assignment/Week_4/Chirag_Week4/findClosestCentroids.m	
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+function idx = findClosestCentroids(X, centroids)
+%FINDCLOSESTCENTROIDS computes the centroid memberships for every example
+%   idx = FINDCLOSESTCENTROIDS (X, centroids) returns the closest centroids
+%   in idx for a dataset X where each row is a single example. idx = m x 1 
+%   vector of centroid assignments (i.e. each entry in range [1..K])
+%
+
+% Set K
+K = size(centroids, 1);
+
+% You need to return the following variables correctly.
+idx = zeros(size(X,1), 1);
+
+% ====================== YOUR CODE HERE ======================
+% Instructions: Go over every example, find its closest centroid, and store
+%               the index inside idx at the appropriate location.
+%               Concretely, idx(i) should contain the index of the centroid
+%               closest to example i. Hence, it should be a value in the 
+%               range 1..K
+%
+% Note: You can use a for-loop over the examples to compute this.
+%
+m = size(X,1);
+
+for i = 1:m,
+  [tp, idx(i)] = min(sum((centroids-X(i,:)).^2,2));
+endfor
+
+
+
+
+
+% =============================================================
+
+end
+
diff --git a/Programming Assignment/Week_4/Chirag_Week4/kMeansInitCentroids.m b/Programming Assignment/Week_4/Chirag_Week4/kMeansInitCentroids.m
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+++ b/Programming Assignment/Week_4/Chirag_Week4/kMeansInitCentroids.m	
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+function centroids = kMeansInitCentroids(X, K)
+%KMEANSINITCENTROIDS This function initializes K centroids that are to be 
+%used in K-Means on the dataset X
+%   centroids = KMEANSINITCENTROIDS(X, K) returns K initial centroids to be
+%   used with the K-Means on the dataset X
+%
+
+% You should return this values correctly
+centroids = zeros(K, size(X, 2));
+
+% ====================== YOUR CODE HERE ======================
+% Instructions: You should set centroids to randomly chosen examples from
+%               the dataset X
+%
+
+centroids = X(randperm(size(X,1), K)', :);
+%for i = 1:K,
+%  centroids(i,:) = X(temp(i),:);
+%endfor
+
+
+
+
+
+% =============================================================
+
+end
+
diff --git a/Programming Assignment/Week_4/Chirag_Week4/projectData.m b/Programming Assignment/Week_4/Chirag_Week4/projectData.m
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+++ b/Programming Assignment/Week_4/Chirag_Week4/projectData.m	
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+function Z = projectData(X, U, K)
+%PROJECTDATA Computes the reduced data representation when projecting only 
+%on to the top k eigenvectors
+%   Z = projectData(X, U, K) computes the projection of 
+%   the normalized inputs X into the reduced dimensional space spanned by
+%   the first K columns of U. It returns the projected examples in Z.
+%
+
+% You need to return the following variables correctly.
+Z = zeros(size(X, 1), K);
+
+% ====================== YOUR CODE HERE ======================
+% Instructions: Compute the projection of the data using only the top K 
+%               eigenvectors in U (first K columns). 
+%               For the i-th example X(i,:), the projection on to the k-th 
+%               eigenvector is given as follows:
+%                    x = X(i, :)';
+%                    projection_k = x' * U(:, k);
+%
+%size(X)
+%size(U)
+%K
+Z = X*U(:,1:K);
+
+
+
+% =============================================================
+
+end
diff --git a/Programming Assignment/Week_4/Chirag_Week4/recoverData.m b/Programming Assignment/Week_4/Chirag_Week4/recoverData.m
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+++ b/Programming Assignment/Week_4/Chirag_Week4/recoverData.m	
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+function X_rec = recoverData(Z, U, K)
+%RECOVERDATA Recovers an approximation of the original data when using the 
+%projected data
+%   X_rec = RECOVERDATA(Z, U, K) recovers an approximation the 
+%   original data that has been reduced to K dimensions. It returns the
+%   approximate reconstruction in X_rec.
+%
+
+% You need to return the following variables correctly.
+X_rec = zeros(size(Z, 1), size(U, 1));
+
+% ====================== YOUR CODE HERE ======================
+% Instructions: Compute the approximation of the data by projecting back
+%               onto the original space using the top K eigenvectors in U.
+%
+%               For the i-th example Z(i,:), the (approximate)
+%               recovered data for dimension j is given as follows:
+%                    v = Z(i, :)';
+%                    recovered_j = v' * U(j, 1:K)';
+%
+%               Notice that U(j, 1:K) is a row vector.
+%               
+
+X_rec = Z*(U(:,1:K)');
+
+% =============================================================
+
+end