This course covers the principles required to develop scalable machine learning pipelines, including a section on Recommendation Systems at Scale which discusses graph-networks, link analysis, collaborative filtering, and challenges of sparsity and scalability.
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.
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