This repository provides materials for an open Machine Learning course, including lecture recordings, slides, and assignments. It is targeted at individuals seeking to learn foundational and advanced ML concepts, offering a structured curriculum with practical exercises.

How It Works

The course covers a range of ML topics, starting with classical methods like Naive Bayes, kNN, and linear models, progressing to more advanced techniques such as SVMs, PCA, decision trees, gradient boosting, and an introduction to deep learning with backpropagation, dropout, and batch normalization. The curriculum is structured weekly, with associated lecture videos, slides, and assignments.

Quick Start & Requirements

• Prerequisites: Access to lecture videos and slides. Recommended reading includes the YSDA ML Book (Russian), "Probabilistic Machine Learning: An Introduction," and the "Deep Learning Book."
• Setup: No specific installation is required to access the course materials.
• Resources: Access to internet for video streaming and reading materials.

Highlighted Details

• Covers classical ML algorithms (Naive Bayes, kNN, Linear Regression, SVM, PCA).
• Includes deep learning fundamentals (Backpropagation, Dropout, Batchnorm, Embeddings, seq2seq).
• Features assignments and practical labs for hands-on learning.
• Provides links to lecture recordings, slides, and supplementary literature.

Maintenance & Community

The course lists several contributors and acknowledges specific individuals and organizations for their support and contributions, including Stanislav Fedotov and YSDA.

Licensing & Compatibility

The repository does not explicitly state a license.

Limitations & Caveats

Some course materials, including the primary ML book recommendation, are only available in Russian. Lecture recordings for specific sessions in 2022 were not conducted due to technical reasons or instructor illness, with alternative sessions or reviews provided.