epfml/OptML_course resource

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1467 stars · 346 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

EPFL's CS-439 'Optimization for Machine Learning' course materials, including lecture slides and lab exercises in Jupyter notebooks. It covers convexity, gradient methods, proximal and stochastic algorithms, duality, second-order methods, and advanced topics like distributed optimization and federated learning.

Use cases

• learn optimization methods for machine learning
• study gradient descent and stochastic gradient descent theory
• find exercises on convex optimization and duality
• self-study a university course on optimization for data science
• prepare for ML interviews covering optimization algorithms
• learn about proximal and subgradient methods
• understand non-convex optimization and neural network training

When to choose

• you want structured, university-grade course material on ML optimization
• you prefer learning with slides plus hands-on notebook exercises
• you need coverage of both convex and non-convex optimization theory

When to avoid

• you need production optimization software or solver libraries
• you want a quick reference rather than a full course
• you need a formally licensed, redistributable resource (no license is specified)

Facets

learning-resource · maturity active

machine-learning math developer-tools machine-learning tutorials education mathematics python cross-platform optimization course-materials convex-optimization gradient-descent epfl jupyter-notebooks lecture-slides federated-learning algorithms

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