# Coursera-Machine-Learning-Stanford is eleven week folders of MATLAB answers, with no licence file and a bug link pointing at another account > Solutions to the programming assignments and quizzes from a machine learning course, laid out as one directory per week. The readme asks for stars and a donation and promises to look at issues, but the repository carries no licence of any kind and the issue link resolves to a different user. **atinesh/Coursera-Machine-Learning-Stanford** — Machine learning-Stanford University - Repository: https://github.com/atinesh/Coursera-Machine-Learning-Stanford - Stars: 1,183 · Forks: 745 - Language: MATLAB - License: not declared - Published: 2026-09-10 · Updated: 2026-09-10 · Language: en - Canonical page: https://hysenlabs.com/projects/atinesh-coursera-machine-learning-stanford ## Eleven week folders, and nothing about how to run any of it The repository is eleven directories and a readme. The directories are named for weeks and numbered through eleven, with a space between the word and the number in each name. Spaces in directory names are a small thing that costs a small thing: every path has to be quoted, and a plain recursive copy into another project will break on the first week. It is also why the ordering looks wrong. The list sorts as one, then ten, then eleven, then two, because there is no zero padding. Anyone scripting a walk over the repository gets the weeks in that order. More consequential is what is not there. There is no readme inside any week folder, no environment description, no MATLAB version, and no statement about datasets. The repository does not say where the data comes from, whether it is included, or whether you are expected to download it from somewhere else. A reader arriving with the course open and the data in hand can use this. A reader arriving cold cannot tell which week folder corresponds to which assignment without opening eleven folders. The one automation directory holds nothing that changes that; there is no build, no test and no package manifest. ## The bug report link points at a different account The closing section invites bug reports and links to an issue tracker. The account in that link is not the account that owns this repository. The repository sits under one user. The issue link in the readme points at a different user name with a suffix appended, and a different path from the repository one. So following the readme's own instruction for reporting a problem sends you somewhere that is not this repository. That is a plausible artefact of a rename, a transfer, or a second account kept for something else, and it is the kind of thing that goes unnoticed because the link works. It resolves. It is just not the tracker for the code you are reading. The practical effect is that the readme's promise to look into issues promptly is attached to a destination that may not reach the person making it. There is no other contact route in the file: no address, no discussion link, no secondary repository. The donation link and the star request are the only other outbound references besides the certificate and the course page. ## A star request and a donation link, with no licence anywhere The support section asks for two things. It asks for a star, on the grounds that it helps others find the project and keeps the author motivated to improve it further. And it offers a way to buy the author a coffee. Nowhere in the repository is a licence stated. There is no licence file at the top level, which contains a single automation directory, the readme and the week folders. No licence name is recorded in the repository metadata either. Those two facts together are the finding. The readme is written in the register of an open source project: stars, contributions, bug reports, a promise of maintenance. But the legal instrument that would let anyone reuse, modify or redistribute the code is absent, and the absence of a licence is not a permissive default. It is the position where the author has kept all rights and granted none. So the code is readable, which is what most visitors want and is enough for the stated purpose of consulting an answer when you are stuck. It is not reusable in a portfolio, a lecture, a company repository or a derived notebook, and nothing in the file says that is a restriction rather than an oversight. ## The certificate link is a personal account artifact One link in the readme is a verified certificate, and it points at a course platform account URL containing a long identifier. That kind of link is personal in a way the other links are not. It identifies one person's completion record on someone else's platform, and the identifier in the path is the handle for that record rather than a stable address for anything. It will not resolve for you, it tells a reader nothing about whether the course is worth taking, and it is the kind of URL that goes stale if the person changes their profile settings. It is also the only evidence in the repository that the work was done rather than assembled. A certificate is a reasonable signal, and it is the sort of thing a repository of solutions includes so that readers know the author completed the material. But it is a claim rather than a record, and unlike a code review or a citation it cannot be checked from the repository itself. The rest of the outbound references are more durable: a link to the course itself, and a single-item reference list that names the course and links to it. ## MATLAB, and no statement about which version or which toolboxes The recorded primary language for the repository is MATLAB, which is the right answer for this material. The course it accompanies was built around MATLAB exercises, so the answers are in the language the assignments were written in rather than translated into whatever the author's day job uses. What the repository does not say is anything about the environment. There is no MATLAB version, no toolbox list, no mention of which functions require an add-on that a student licence does not include. Several of the standard exercises in this kind of course depend on specific toolboxes for loading data and for the demonstration datasets, and nothing here says which. The consequence is that the code is readable but not necessarily runnable. If you have a recent MATLAB and the right toolboxes, opening a week folder and reading the script tells you what the assignment was asking and what the intended answer looks like. If your licence is missing a toolbox, or you have migrated to a numerical language where the plotting and matrix idioms differ, you are reading rather than running, and the scripts will not tell you which of those two situations you are in. ## The stated method is to attempt the assignment before reading the answer The readme is short and its advice is unambiguous. It describes the contents as the solution to all the programming assignments and all the quizzes, then tells you to try to solve the assignments yourself first and to browse the code only if you get stuck. That framing is the project's whole design. There is no partial solution, no hints file, no difficulty marker telling you which weeks are hard. The unit of value is the individual answer, and the intended consumption pattern is attempt, fail, consult one file, move on. The contents list is three items: the lecture slides, the solution to the programming assignment, and the solution to the quizzes. So each week folder is expected to hold all three, which means the folder name tells you nothing about which file you want and you have to look inside. The reference list has one entry, the course itself. There is no paper behind any of the assignments, no link to a write-up, and no discussion of why a particular answer is the right one. For a course that covers the standard supervised learning material that is a reasonable scope, since the course is the reference, but it does mean the repository cannot be read as an explanation of the algorithms. ## Conclusion Read it as a set of worked answers rather than as a project, because that is what it is: eleven folders and a short readme, with nothing to install and nothing to configure. The value depends entirely on doing the assignments yourself first, which the author asks for explicitly. Two things to settle before you rely on it. There is no licence, which means the default is that nobody has granted you permission to reuse the code even though you can read it. And the issue link in the readme points at a different user account, so filing a report there may not reach the person who maintains this copy. The last commit is dated 2026-06-06. ## FAQ ### What is in the Coursera-Machine-Learning-Stanford repository? Eleven directories named for course weeks, each described as holding lecture slides, the solution to the programming assignment, and the solution to the quizzes, plus one readme. ### Which programming language are the solutions written in? MATLAB, which is the recorded primary language for the repository. No MATLAB version or required toolbox is stated anywhere in the files. ### Can I reuse the code from this course solutions repository? Nothing in the repository grants permission. There is no licence file at the top level and no licence name recorded in the repository metadata, so the code is readable but not licensed for reuse. ### Where do I report a bug in this repository? The readme links to an issue tracker under a different user account than the one hosting the repository, so the link it gives does not point at the tracker for this copy. ### How should I use these course solutions? The author asks that you attempt the assignments yourself first and consult the code only where you get stuck, so the value is in one file at a time rather than in reading a week end to end. ## Sources - [atinesh/Coursera-Machine-Learning-Stanford on GitHub](https://github.com/atinesh/Coursera-Machine-Learning-Stanford) - [Issues](https://github.com/atinesh/Coursera-Machine-Learning-Stanford/issues) - [README](https://github.com/atinesh/Coursera-Machine-Learning-Stanford/blob/master/README.md) --- Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/atinesh-coursera-machine-learning-stanford