This repository provides materials for the EPFL CS-439 Optimization for Machine Learning course, targeting students and researchers interested in modern optimization techniques for large-scale machine learning and data science. It offers lecture slides, weekly Python exercises with solutions, and a project description focused on empirical analysis of optimization algorithms.

How It Works

The course covers a broad spectrum of optimization methods, from foundational concepts like convexity and gradient descent to advanced topics such as accelerated methods, primal-dual formulations, second-order methods, and non-convex optimization. A key focus is on the scalability of these algorithms to large datasets, with practical implementations in Python for exercises and a project.

Quick Start & Requirements

• Installation: Primarily involves cloning the repository and setting up a Python environment.
• Prerequisites: Python, with specific libraries likely required for the exercises (e.g., NumPy, SciPy, Matplotlib, PyTorch/TensorFlow for ML tasks). No explicit installation commands are provided, but exercise solutions suggest a Python-based workflow.
• Resources: Requires a standard development environment capable of running Python code.

Highlighted Details

• Comprehensive coverage of optimization algorithms relevant to ML.
• Practical Python exercises with provided solutions.
• Mini-project encouraging empirical investigation of algorithms.
• Links to past exams and solutions for assessment preparation.
• Curated list of recommended textbooks and related courses.

Maintenance & Community

The repository is associated with EPFL and its instructors/assistants. Community interaction is likely channeled through an EPFL-internal discussion forum.

Licensing & Compatibility

The repository content is presented as course materials. Specific licensing terms are not detailed in the README, which may impact commercial use or redistribution.

Limitations & Caveats

The repository primarily serves as course documentation and exercise material; it is not a standalone library or framework. Some resources, like the discussion forum, may be EPFL-internal. The focus is on educational content rather than production-ready code.