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Course materials for a graduate-level class on optimization for machine learning, including lecture notes and problem sets.

This comprehensive lesson teaches how to test ML artifacts including code, data, and models to build a reliable ML system. It covers the intuition behind testing, different types of tests (unit, integration, system, acceptance, regression), best practices, and implementation details for testing code, data expectations, and model behavior.

This course introduces the theoretical foundations and algorithmic developments in stochastic optimization for machine learning. It covers basic convex optimization theories and focuses on stochastic approximation and its accelerations in statistical and machine learning models.