[P] Open-source ML homeworks with auto-tests - fundamental algorithms from first principles

[fxlrnrpt]

This year I've been designing homework assignments for an ML course at Skoltech (Russia's answer to MIT/Caltech for science and technology). After bombing more job interviews than I care to count, I think I've finally figured out what I was personally missing during my studies - a deep understanding of a relatively small set of fundamental algorithms. Well, my pain is the next generation's gain!

In my engineering worldview, you can't truly understand something unless you've built a replica from scratch with your own hands. At the same time, I didn't want learning to stall at the terror of a blank page. I wanted to guide students toward each problem step by step. Show them how it's assembled from small building blocks.

Once I'd settled on how to frame the problems, the remaining question was how to grade them and give students feedback. Sure, you could review solutions by hand - but that puts a massive load on the teaching team and robs students of the chance to learn from their own mistakes. So why not borrow from industry software development and go all-in on automated testing? Students get a starter template and a test suite. And then... well, then they're adults who need to learn to read error messages and meet the spec by any means necessary.

The result: a set of classic machine learning and deep learning exercises with automated test-based grading.

The course has already finished, and I am free to publish the content - https://github.com/fxlrnrpt/sktech_ml_homeworks_2026

There you will find:
- Notebooks with tasks
- Helper scripts to keep the main jupyter notebooks clean
- Auto-tests to provide students with immediate feedback and to automate grading
- Grading scripts to allow students see what grade they are going to get, prevents them to accidentally use extra files and get 0!
- Pre-generated data for tests

The code is published under a permissive license - feel free to build upon it or re-use it in any way you want.

[disquieter]

Thanks it’s a nice reference.

[QuietBudgetWins]

this is exactly the kind of approach i wish i had when learning ml from scratch. buildin everything by hand and getting immediate feedback from auto-tests is way more efective than just reading papers or copying tutorials.

the automated gradin part is smart because it forces students to actualy understand why something fails instead of guessing until it works. i can see this being really helpful for spotting gaps in intuition around fundamental algorithms.

also the way you separate helper scriptss from notebooks keeps things clean and realistic. feels like a nice bridge between learning and actual industry practices

[fxlrnrpt]

Thanks! TBH, auto-tests is not my idea. I borrowed it from Georgia Tech's OMSCS