Ml-Course-Msu

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ml-course-msu is a Research & Education tool that provides lecture notes and code for a Machine Learning practical course. It offers materials for seminars at CMC MSU, including theoretical concepts and practical assignments.

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Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is ml-course-msu?

ml-course-msu is a GitHub repository offering comprehensive lecture notes and code for a practical Machine Learning course at CMC MSU. It serves as a valuable resource for students and educators, covering a wide range of topics from linear methods and metric methods to neural networks, Bayesian methods, and gradient boosting. The repository includes detailed lecture notes, code examples, and practical assignments with defined deadlines. It also provides information on grading rules, contest links, and contact details for assignment submissions, making it a complete educational package for machine learning studies.

Best used for

Ideal for professors and teachers who need to provide comprehensive machine learning course materials, practical assignments, and code examples. Especially valuable for educators seeking to share structured content and exercises for university-level machine learning programs.

Common actions

access course materials

learn machine learning

share educational resources

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Capabilities

Key features

Lecture notes
Code examples
Practical assignments
Grading rules
Contest links

Target Audience

professorteacher

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What topics are covered in the ml-course-msu materials?

The course materials cover a broad spectrum of machine learning topics, including linear and metric methods, gradient descent, regularization, linear classification models, logistic regression, decision trees, neural networks, Bayesian methods, and Gaussian processes.

Are there practical assignments included in the course?

Yes, the course includes several practical assignments with both soft and hard deadlines. Students are expected to complete these assignments to achieve a passing grade, with penalties for late submissions after the soft deadline.

How are grades determined for the course?

Grades are based on theoretical tests, practical assignments, and contests. To pass, students must achieve at least 80% on each theoretical topic and practical assignment. Top performers in the rating may receive an additional point on the exam.

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