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Explore fundamental concepts in machine learning theory through this lecture delivered by Karthik Sridharan at the International Centre for Theoretical Sciences. Delve into the mathematical foundations that underpin modern data science and machine learning algorithms as part of the comprehensive "Data Science: Probabilistic and Optimization Methods II" program. Learn about core theoretical principles that enable current successes and future breakthroughs in machine learning, with particular emphasis on how rigorous theory informs the development of robust and adaptable systems. Gain insights into the probabilistic and optimization methods that form the backbone of contemporary data science applications. This lecture forms part of an intensive program featuring bootcamp sessions on foundational topics in probability, statistics, and optimization, followed by advanced tutorials covering cutting-edge developments in reinforcement learning, generative modeling, causal inference, and advanced probability theory. Benefit from the expertise of leading researchers and practitioners as they explore the evolving theoretical landscape of data science and its practical applications in modern machine learning systems.
