Explore powerful algorithmic tools for tackling large-scale data problems in machine learning and optimization. Gain a solid foundation for research in this cutting-edge field.
University CoursesMachine Learning
Introduction
This course covers a variety of topics from optimization (convex, nonconvex, continuous and combinatorial) as well as streaming algorithms. The key aim is to make students aware of powerful algorithmic tools used for tackling large-scale data intensive problems. The topics covered are chosen to give students a solid footing for research in machine learning and optimization, while strengthening their practical grasp.
Highlights
Covers foundational classical theory as well as cutting-edge material in large-scale optimization
Focuses on making students aware of powerful algorithmic tools for tackling large-scale data problems
Provides a solid footing for research in machine learning and optimization
Recommendation
This course is recommended for students with prior exposure to convex optimization and algorithms at a graduate level, as well as a strong working knowledge of linear algebra, analysis, probability, and statistics. Programming experience in high-level languages is also advantageous.
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