Introduction

Submitting the classic Coursera Machine Learning assignments in Octave is mostly about following the provided workflow exactly. Most submission failures are not caused by the math itself, but by editing the wrong files, changing function signatures, running from the wrong directory, or submitting before the grader script can find your completed functions.

How the Assignment Structure Usually Works

The assignment bundle typically contains:

• one or more .m files where you complete specific functions
• helper files and datasets
• a submit.m script that packages and uploads your answers
• instructions describing which functions you are allowed to edit

The grader normally does not want a zip file or screenshots. It wants the outputs of the required functions generated in the expected format.

That is why staying close to the starter structure matters.

Set Up Octave Correctly

Start by extracting the assignment files into a clean folder and launching Octave in that directory.

pwd
ls

You should be able to see the assignment files, including submit.m.

If needed, change into the assignment directory explicitly:

cd '/path/to/ex1'
ls

Running the submission script from the wrong directory is one of the easiest ways to get confusing file-not-found errors.

Edit Only the Intended Functions

The starter files usually mark the sections you are expected to change. Keep the function names, input arguments, and return values exactly as given unless the assignment instructions explicitly say otherwise.

For example, a typical exercise file might look like this:

1function J = computeCost(X, y, theta)
2  m = length(y);
3
4  predictions = X * theta;
5  sqErrors = (predictions - y) .^ 2;
6  J = 1 / (2 * m) * sum(sqErrors);
7end

This kind of function is safe to edit because you are filling in the algorithm while keeping the contract intact.

What you should not do is rename the function, reorder the arguments, or change its return type because the grader expects the original interface.

Test Before You Submit

Most assignment bundles include helper commands or checks that make local testing possible. Run the script segments the course provides before submitting.

For example:

1X = [1; 2; 3];
2y = [1; 2; 3];
3theta = 1;
4
5computeCost(X, y, theta)

You should also run the main exercise script when provided, because it often checks whether your functions produce expected intermediate values.

ex1

If the assignment script crashes locally, the submission is unlikely to succeed remotely.

Use submit.m the Way the Course Expects

The actual submission step in the classic workflow is usually:

submit

or:

submit()

The script may prompt for email, token, or assignment part selection depending on the course version. Follow the prompts exactly and wait for confirmation.

The grader often evaluates each required function separately. That means one broken part does not always invalidate everything, but it can still reduce your score or block later checks.

Keep Your Code Compatible with the Grader

Autograders are stricter than human reviewers. They care about reproducible outputs and expected interfaces.

A few habits help:

• do not print extra debugging output unless the assignment asks for it
• do not remove starter comments that affect script flow
• avoid relying on local files or packages not included in the assignment
• prefer vectorized Octave operations when the exercise expects them

For example, instead of writing a slow loop when matrix operations are intended, use built-in array operations.

predictions = X * theta;
errors = predictions - y;
grad = (1 / m) * (X' * errors);

This is both idiomatic and more likely to match the course's intended solution path.

Troubleshooting Submission Failures

If submission fails, work through the basics in order:

1. confirm you are in the correct assignment directory
2. confirm the required function files still have their original names
3. run the local exercise script again
4. remove accidental syntax errors or unmatched end statements
5. retry the submission after confirming internet access

If the grader reports the wrong answer even though the code "looks right," check dimensions, vectorization, and whether you modified any starter interfaces.

Version and Course Variations

Coursera courses change over time. Some current machine learning courses use Python rather than Octave. If you are working from a classic Octave-based assignment pack, trust the files bundled with that assignment more than old forum posts or random snippets online.

The local assignment materials are the source of truth for that specific exercise.

Common Pitfalls

A common mistake is editing more than the marked sections and accidentally breaking the grader's expected function signatures.

Another issue is submitting from the wrong folder, which causes the submission script to miss required files.

Learners also often leave debugging prints in place. Those may not always break grading, but they make troubleshooting harder.

Finally, do not assume a mathematically correct solution will pass if it returns results in the wrong shape. Coursera graders are usually strict about dimensions and interfaces.

Summary

• Work from the provided assignment files and keep their structure intact.
• Edit only the intended functions and preserve their signatures.
• Test locally before running submit.
• Run the submission script from the correct assignment directory.
• Most submission problems come from workflow mistakes, not from the ML formulas themselves.