Machine Learning

All course materials are available in the following Google Classroom:
https://classroom.google.com/c/ODMwNDU0OTExODY1?cjc=eswzxamc

The course is a theoretical–practical introduction covering the fundamentals of modern machine learning: problem formulation (classification, regression, ranking), preprocessing pipelines, evaluation (metrics and experimental protocols), regularization, optimization, linear models, trees, ensembles, and an introduction to neural models for deep learning. Particular attention is given to good experimental practices, data management, and principles for making experiments reproducible, including practical guidance on using the newly acquired machines: copying and synchronizing code via GitHub on the purchased machines, and creating and managing isolated Python environments (virtualenv/conda) to ensure reproducibility and consistency of software installations.