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Super Data Science: ML & AI Podcast with Jon Krohn
813: Solving Business Problems Optimally with Data, with Jerry Yurchisin
Aug 27, 2024
Jerry Yurchisin, a mathematical optimization expert from Gurobi, joins the conversation to explore the power of optimization in solving complex business challenges. He discusses engaging examples like the Burrito Optimization Game, explaining its real-world applications. Key differences between machine learning and optimization are highlighted, emphasizing how the latter can provide clear prescriptive solutions. Yurchisin also touches on the integration of large language models in optimization, advancements in GPU technology, and the complexities of NP-hard problems.
01:43:30
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Quick takeaways
- Mathematical optimization surpasses machine learning in solving real-world problems with complex constraints, offering prescriptive decision-making capabilities.
- The Burrito Optimization Game exemplifies how interactive tools can effectively teach the principles of optimization and decision-making complexity.
Deep dives
Understanding Mathematical Optimization
Mathematical optimization is a crucial method in data science that focuses on making prescriptive decisions, distinguishing it from predictive analytics like machine learning. This approach excels when solving complex real-world issues characterized by numerous constraints, as it maximizes or minimizes specific outcomes, such as costs or profits. The insights illustrate how optimization differs from traditional statistical methods by modeling logic through algebra rather than merely predicting future outcomes based on historical data. Thus, those interested in enhancing commercial decision-making can leverage mathematical optimization for impactful results.
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