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Explore advanced techniques for modeling noisy count data in this comprehensive lecture from the "Machine Learning for Health and Disease" program. Delve into statistical methods and machine learning approaches specifically tailored for analyzing count-based datasets with inherent noise. Learn from expert Sayan Mukherjee as he covers fundamental concepts, practical applications, and cutting-edge strategies for handling this challenging data type. Gain valuable insights applicable to various fields, including biomedicine, public health, and clinical research. This lecture is part of a broader program aimed at bridging the gap between mathematical modeling and clinical problems, making it ideal for PhD students in STEM fields, medical professionals, and researchers interested in applying machine learning techniques to health-related data analysis.
