sktech_ml_homeworks_2026

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sktech_ml_homeworks_2026

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# ML2026 homeworks

This is a repository containing the homeworks for the Machine Learning course (MA060018) in Skoltech, which is held at Term 3, 2026. Feel free to build upon it and re-use it in any way you want.

## The list of the current homeworks published:

- Homework 1 - Regression & Classification
- Homework 2 - Decision Trees & Ensembles
- Homework 3 - Deep Learning
- Homework 4 - Dimensionality Reduction

## Homework instructions

1. Install [uv](https://docs.astral.sh/uv/getting-started/installation/)
2. Run `uv sync` from the root of the folder with this home task
   1. You will have to add `uv` to `Path` on Windows!
3. Open the jupyter notebook with the task in VS Code and have some fun!

## Note for teachers

For each homework, students received an archive with:
- hwx_xxxx.ipynb - notebook with tasks
- x_helpers.py - to keep the main jupyter notebook clean
- x_test.py - auto-tests to provide students with immediate feedback and to automate grading
- grade_hw*.py - a script to allow students see what grade they are going to get, prevents them to accidentally use extra files and get 0!
- test_data/* - pre-generated data for tests

You might notice that we have a separate script for test data generation for each homework - generate_test_data.py. The rationale here is to prevent students from easily reverse engineering the tests and overfitting to them without solving the actual task.

We also conducted a short written in-person quiz after completion of each homework - a must-have in the world where Claude Code exists!

## Course Description:

The course is a general introduction to machine learning (ML) and its applications. It covers fundamental topics in ML and describes the most important algorithmic basis and tools. It also provides important aspects of the algorithms’ applications. The course starts with an overview of canonical ML applications and problems, learning scenarios, etc. Next, we discuss in-depth fundamental ML algorithms for classification, regression, clustering, etc., their properties, and practical applications. The last part of the course is devoted to advanced ML topics such as Gaussian processes, neural networks. Within practical sections, we show how to use the ML methods and tune their hyper-parameters. Home assignments include the application of existing algorithms to solve data analysis problems. The students are assumed to be familiar with basic concepts in linear algebra, probability, real analysis, optimization, and python programming.

On completion of the course students are expected to:

- Have a good understanding of the fundamental issues and challenges of ML: data, model selection, model complexity among others;
- Have an understanding of the strengths and weaknesses of many popular ML approaches;
- Appreciate the basic underlying mathematical relationships within and across ML algorithms and the paradigms of supervised and unsupervised learning.
- Be able to design and implement various machine learning algorithms in a range of real-world applications.

## Lectures & Seminars:

- [Lectures](github.com/adasegroup/ML_2026_Lectures)
- [Seminars](https://github.com/adasegroup/ML_2026_Seminars)

sktech_ml_homeworks_2026

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