feat: add Stanford ML course exercises and supporting files

Author: Joe | GJoeG&MM

Certifications/Stanford Machine Learning/ex1/token.mat

@@ -0,0 +1,15 @@
+# Created by Octave 10.2.0, Tue Jun 10 08:32:55 2025 UTC <unknown@JOE-DIAQDC4>
+# name: email
+# type: sq_string
+# elements: 1
+# length: 21
+lorenzosca7@gmail.com
+
+
+# name: token
+# type: sq_string
+# elements: 1
+# length: 16
+Ak3sUtRKkXjNx8m0
+
+

Certifications/Stanford Machine Learning/ex2/costFunction.m

@@ -20,10 +20,14 @@
 % Note: grad should have the same dimensions as theta
 %
 
-h_theta = sigmoid(X*theta);
-J = (1 / m) * ((-y' * log(h_theta)) - (1 - y)' * log(1 - h_theta));
+% Compute hypothesis using sigmoid function
+h = sigmoid(X * theta);
 
-grad = (1 / m) * (h_theta - y)' * X;
+% Calculate cost function
+J = (1/m) * sum(-y .* log(h) - (1 - y) .* log(1 - h));
+
+% Calculate gradient
+grad = (1/m) * (X' * (h - y));
 
 % =============================================================
 

Certifications/Stanford Machine Learning/ex2/costFunctionReg.m

@@ -11,11 +11,27 @@
 J = 0;
 grad = zeros(size(theta));
 
-h_theta = sigmoid(X*theta);
-J = (1/m) * (-y' * log(h_theta) - (1-y)' * log(1-h_theta)) + (lambda/(2*m)) * (theta(2:length(theta)))' * theta(2:length(theta));
+% ====================== YOUR CODE HERE ======================
+% Instructions: Compute the cost of a particular choice of theta.
+%               You should set J to the cost.
+%               Compute the partial derivatives and set grad to the partial
+%               derivatives of the cost w.r.t. each parameter in theta
 
-thetaZero = theta;
-thetaZero(1) = 0;
+% Compute hypothesis using sigmoid function
+h = sigmoid(X * theta);
 
-grad = ((1 / m) * (h_theta - y)' * X) + lambda / m * thetaZero';
-end
+% Calculate cost function with regularization term
+% Note that theta(1) is not regularized
+J = (1/m) * sum(-y .* log(h) - (1 - y) .* log(1 - h)) + (lambda/(2*m)) * sum(theta(2:end).^2);
+
+% Calculate gradient with regularization
+% First calculate the gradient without regularization
+grad_no_reg = (1/m) * (X' * (h - y));
+
+% Add regularization term to all theta values except theta(1)
+grad = grad_no_reg;
+grad(2:end) = grad(2:end) + (lambda/m) * theta(2:end);
+
+% =============================================================
+
+end

Certifications/Stanford Machine Learning/ex2/ex2.m

@@ -24,8 +24,7 @@
 %  contains the label.
 
 data = load('ex2data1.txt');
-X = data(:, [1, 2]); %Feature 1 and Feature 2
-y = data(:, 3); %Negative or Positive
+X = data(:, [1, 2]); y = data(:, 3);
 
 %% ==================== Part 1: Plotting ====================
 %  We start the exercise by first plotting the data to understand the 
@@ -59,7 +58,7 @@
 [m, n] = size(X);
 
 % Add intercept term to x and X_test
-X = [ones(m, 1) X]; %Concatenating Column Vector 1's(Feature 0) with Column Vector X (Feature 1, Feature 2)
+X = [ones(m, 1) X];
 
 % Initialize fitting parameters
 initial_theta = zeros(n + 1, 1);
@@ -68,12 +67,25 @@
 [cost, grad] = costFunction(initial_theta, X, y);
 
 fprintf('Cost at initial theta (zeros): %f\n', cost);
+fprintf('Expected cost (approx): 0.693\n');
 fprintf('Gradient at initial theta (zeros): \n');
 fprintf(' %f \n', grad);
+fprintf('Expected gradients (approx):\n -0.1000\n -12.0092\n -11.2628\n');
+
+% Compute and display cost and gradient with non-zero theta
+test_theta = [-24; 0.2; 0.2];
+[cost, grad] = costFunction(test_theta, X, y);
+
+fprintf('\nCost at test theta: %f\n', cost);
+fprintf('Expected cost (approx): 0.218\n');
+fprintf('Gradient at test theta: \n');
+fprintf(' %f \n', grad);
+fprintf('Expected gradients (approx):\n 0.043\n 2.566\n 2.647\n');
 
 fprintf('\nProgram paused. Press enter to continue.\n');
 pause;
 
+
 %% ============= Part 3: Optimizing using fminunc  =============
 %  In this exercise, you will use a built-in function (fminunc) to find the
 %  optimal parameters theta.
@@ -85,13 +97,14 @@
 %  This function will return theta and the cost 
 [theta, cost] = ...
 	fminunc(@(t)(costFunction(t, X, y)), initial_theta, options);
-	% 't' is passed as dummy parameter which is initialized with 'initial_theta' 
-	% first then subsequent values are choosen by fminunc
 
 % Print theta to screen
 fprintf('Cost at theta found by fminunc: %f\n', cost);
+fprintf('Expected cost (approx): 0.203\n');
 fprintf('theta: \n');
 fprintf(' %f \n', theta);
+fprintf('Expected theta (approx):\n');
+fprintf(' -25.161\n 0.206\n 0.201\n');
 
 % Plot Boundary
 plotDecisionBoundary(theta, X, y);
@@ -125,13 +138,14 @@
 
 prob = sigmoid([1 45 85] * theta);
 fprintf(['For a student with scores 45 and 85, we predict an admission ' ...
-         'probability of %f\n\n'], prob);
+         'probability of %f\n'], prob);
+fprintf('Expected value: 0.775 +/- 0.002\n\n');
 
 % Compute accuracy on our training set
 p = predict(theta, X);
 
 fprintf('Train Accuracy: %f\n', mean(double(p == y)) * 100);
+fprintf('Expected accuracy (approx): 89.0\n');
+fprintf('\n');
 
-fprintf('\nProgram paused. Press enter to continue.\n');
-pause;
 

Certifications/Stanford Machine Learning/ex2/ex2.pdf

@@ -2,7 +2,7 @@
 %
 %  Instructions
 %  ------------
-% 
+%
 %  This file contains code that helps you get started on the second part
 %  of the exercise which covers regularization with logistic regression.
 %
@@ -29,7 +29,7 @@
 
 plotData(X, y);
 
-% Put some labels 
+% Put some labels
 hold on;
 
 % Labels and Legend
@@ -43,8 +43,8 @@
 
 %% =========== Part 1: Regularized Logistic Regression ============
 %  In this part, you are given a dataset with data points that are not
-%  linearly separable. However, you would still like to use logistic 
-%  regression to classify the data points. 
+%  linearly separable. However, you would still like to use logistic
+%  regression to classify the data points.
 %
 %  To do so, you introduce more features to use -- in particular, you add
 %  polynomial features to our data matrix (similar to polynomial
@@ -68,13 +68,33 @@
 [cost, grad] = costFunctionReg(initial_theta, X, y, lambda);
 
 fprintf('Cost at initial theta (zeros): %f\n', cost);
+fprintf('Expected cost (approx): 0.693\n');
+fprintf('Gradient at initial theta (zeros) - first five values only:\n');
+fprintf(' %f \n', grad(1:5));
+fprintf('Expected gradients (approx) - first five values only:\n');
+fprintf(' 0.0085\n 0.0188\n 0.0001\n 0.0503\n 0.0115\n');
+
+fprintf('\nProgram paused. Press enter to continue.\n');
+pause;
+
+% Compute and display cost and gradient
+% with all-ones theta and lambda = 10
+test_theta = ones(size(X,2),1);
+[cost, grad] = costFunctionReg(test_theta, X, y, 10);
+
+fprintf('\nCost at test theta (with lambda = 10): %f\n', cost);
+fprintf('Expected cost (approx): 3.16\n');
+fprintf('Gradient at test theta - first five values only:\n');
+fprintf(' %f \n', grad(1:5));
+fprintf('Expected gradients (approx) - first five values only:\n');
+fprintf(' 0.3460\n 0.1614\n 0.1948\n 0.2269\n 0.0922\n');
 
 fprintf('\nProgram paused. Press enter to continue.\n');
 pause;
 
 %% ============= Part 2: Regularization and Accuracies =============
 %  Optional Exercise:
-%  In this part, you will get to try different values of lambda and 
+%  In this part, you will get to try different values of lambda and
 %  see how regularization affects the decision coundart
 %
 %  Try the following values of lambda (0, 1, 10, 100).
@@ -112,5 +132,5 @@
 p = predict(theta, X);
 
 fprintf('Train Accuracy: %f\n', mean(double(p == y)) * 100);
-
+fprintf('Expected accuracy (with lambda = 1): 83.1 (approx)\n');
 

Certifications/Stanford Machine Learning/ex2/ex2_reg.m

@@ -22,15 +22,17 @@ function submitWithConfiguration(conf)
     response = submitParts(conf, email, token, parts);
   catch
     e = lasterror();
-    fprintf( ...
-      '!! Submission failed: unexpected error: %s\n', ...
-      e.message);
-    fprintf('!! Please try again later.\n');
+    fprintf('\n!! Submission failed: %s\n', e.message);
+    fprintf('\n\nFunction: %s\nFileName: %s\nLineNumber: %d\n', ...
+      e.stack(1,1).name, e.stack(1,1).file, e.stack(1,1).line);
+    fprintf('\nPlease correct your code and resubmit.\n');
     return
   end
 
   if isfield(response, 'errorMessage')
     fprintf('!! Submission failed: %s\n', response.errorMessage);
+  elseif isfield(response, 'errorCode')
+    fprintf('!! Submission failed: %s\n', response.message);
   else
     showFeedback(parts, response);
     save(tokenFile, 'email', 'token');
@@ -62,13 +64,13 @@ function submitWithConfiguration(conf)
 function response = submitParts(conf, email, token, parts)
   body = makePostBody(conf, email, token, parts);
   submissionUrl = submissionUrl();
-  params = {'jsonBody', body};
-  responseBody = urlread(submissionUrl, 'post', params);
-  response = loadjson(responseBody);
+  responseBody = getResponse(submissionUrl, body);
+  jsonResponse = validateResponse(responseBody);
+  response = loadjson(jsonResponse);
 end
 
 function body = makePostBody(conf, email, token, parts)
-  bodyStruct.assignmentSlug = conf.assignmentSlug;
+  bodyStruct.assignmentKey = conf.assignmentKey;
   bodyStruct.submitterEmail = email;
   bodyStruct.secret = token;
   bodyStruct.parts = makePartsStruct(conf, parts);
@@ -100,26 +102,81 @@ function showFeedback(parts, response)
   fprintf('== \n');
   fprintf('== %43s | %9s | %-s\n', 'Part Name', 'Score', 'Feedback');
   fprintf('== %43s | %9s | %-s\n', '---------', '-----', '--------');
+
   for part = parts
     score = '';
     partFeedback = '';
-    partFeedback = response.partFeedbacks.(makeValidFieldName(part{:}.id));
-    partEvaluation = response.partEvaluations.(makeValidFieldName(part{:}.id));
+    % NEW PARSING REPONSE BODY
+    disp(response)
+    partFeedback = response.linked.onDemandProgrammingScriptEvaluations_0x2E_v1{1}(1).parts.(makeValidFieldName(part{:}.id)).feedback;
+    partEvaluation = response.linked.onDemandProgrammingScriptEvaluations_0x2E_v1{1}(1).parts.(makeValidFieldName(part{:}.id));
     score = sprintf('%d / %3d', partEvaluation.score, partEvaluation.maxScore);
     fprintf('== %43s | %9s | %-s\n', part{:}.name, score, partFeedback);
   end
-  evaluation = response.evaluation;
+  evaluation = response.linked.onDemandProgrammingScriptEvaluations_0x2E_v1{1}(1);
   totalScore = sprintf('%d / %d', evaluation.score, evaluation.maxScore);
   fprintf('==                                   --------------------------------\n');
   fprintf('== %43s | %9s | %-s\n', '', totalScore, '');
   fprintf('== \n');
 end
 
+% use urlread or curl to send submit results to the grader and get a response
+function response = getResponse(url, body)
+  % NEW CURL SUBMISSION FOR WINDOWS AND MAC
+  if ispc
+    new_body = regexprep (body, '\"', '\\"'); % will escape double quoted objects to format properly for windows libcurl
+    json_command = sprintf('curl -X POST -H "Cache-Control: no-cache" -H "Content-Type: application/json" -d "%s" --ssl-no-revoke "%s"', new_body, url);
+    [code, response] = dos(json_command); %dos is for windows
+
+    new_response = regexp(response, '\{(.)*', 'match');
+    response = new_response{1,1};
+
+    % test the success code
+    if (code ~= 0)
+      fprintf('[error] submission with Invoke-WebRequest() was not successful\n');
+    end
+  else
+    json_command = sprintf('curl -X POST -H "Cache-Control: no-cache" -H "Content-Type: application/json" -d '' %s '' --ssl-no-revoke ''%s''', body, url);
+    [code, response] = system(json_command);
+    % test the success code
+    if (code ~= 0)
+      fprintf('[error] submission with curl() was not successful\n');
+    end
+  end
+end
+
+% validate the grader's response
+function response = validateResponse(resp)
+  % test if the response is json or an HTML page
+  isJson = length(resp) > 0 && resp(1) == '{';
+  isHtml = findstr(lower(resp), '<html');
+
+  if (isJson)
+    response = resp;
+  elseif (isHtml)
+    % the response is html, so it's probably an error message
+    printHTMLContents(resp);
+    error('Grader response is an HTML message');
+  else
+    error('Grader sent no response');
+  end
+end
+
+% parse a HTML response and print it's contents
+function printHTMLContents(response)
+  strippedResponse = regexprep(response, '<[^>]+>', ' ');
+  strippedResponse = regexprep(strippedResponse, '[\t ]+', ' ');
+  fprintf(strippedResponse);
+end
+
+
+
+
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 %
 % Service configuration
 %
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 function submissionUrl = submissionUrl()
-  submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1';
+  submissionUrl = 'https://www.coursera.org/api/onDemandProgrammingScriptSubmissions.v1?includes=evaluation';
 end

Certifications/Stanford Machine Learning/ex2/lib/submitWithConfiguration.m

@@ -6,13 +6,23 @@ function plotData(X, y)
 % Create New Figure
 figure; hold on;
 
-% Find Indices of Positive and Negative Examples
-pos = find(y == 1); neg = find(y == 0);
-% Plot Examples
-plot(X(pos, 1), X(pos, 2), 'k+','LineWidth', 2, ...
-'MarkerSize', 7);
-plot(X(neg, 1), X(neg, 2), 'ko', 'MarkerFaceColor', 'y', ...
-'MarkerSize', 7);
+% ====================== YOUR CODE HERE ======================
+% Instructions: Plot the positive and negative examples on a
+%               2D plot, using the option 'k+' for the positive
+%               examples and 'ko' for the negative examples.
+%
+
+
+
+
+
+
+
+
+
+% =========================================================================
+
+
 
 hold off;
 

Certifications/Stanford Machine Learning/ex2/plotData.m

@@ -9,6 +9,20 @@
 % You need to return the following variables correctly
 p = zeros(m, 1);
 
-p = sigmoid(X * theta) >= 0.5;
+% ====================== YOUR CODE HERE ======================
+% Instructions: Complete the following code to make predictions using
+%               your learned logistic regression parameters. 
+%               You should set p to a vector of 0's and 1's
+%
+
+% Calculate the probability using sigmoid function
+prob = sigmoid(X * theta);
+
+% Convert probabilities to binary predictions (0 or 1)
+% Predict 1 if probability >= 0.5, otherwise predict 0
+p = prob >= 0.5;
+
+% =========================================================================
+
 
 end