813: Solving Business Problems Optimally with Data, with Jerry Yurchisin

Jerry Yurchisin from Gurobi joins Jon Krohn to break down mathematical optimization, showing why it often outshines machine learning for real-world challenges. Find out how innovations like NVIDIA’s latest CPUs are speeding up solutions to problems like the Traveling Salesman in seconds.

An episode of the Super Data Science: ML & AI Podcast with Jon Krohn podcast, hosted by Jon Krohn, titled "813: Solving Business Problems Optimally with Data, with Jerry Yurchisin" was published on August 27, 2024 and runs 103 minutes.

August 27, 2024 ·103m · Super Data Science: ML & AI Podcast with Jon Krohn

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Summary

Jerry Yurchisin from Gurobi joins Jon Krohn to break down mathematical optimization, showing why it often outshines machine learning for real-world challenges. Find out how innovations like NVIDIA’s latest CPUs are speeding up solutions to problems like the Traveling Salesman in seconds. Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: • The Burrito Optimization Game and mathematical optimization use cases [03:36] • Key differences between machine learning and mathematical optimization [05:45] • How mathematical optimization is ideal for real-world constraints [13:50] • Gurobi’s APIs and the ease of integrating them [21:33] • How LLMs like GPT-4 can help with optimization problems [39:39] • Why integer variables are so complex to model [01:02:37] • NP-hard problems [01:11:01] • The history of optimization and its early applications [01:26:23] Additional materials: www.superdatascience.com/813

Episode Description

Jerry Yurchisin from Gurobi joins Jon Krohn to break down mathematical optimization, showing why it often outshines machine learning for real-world challenges. Find out how innovations like NVIDIA’s latest CPUs are speeding up solutions to problems like the Traveling Salesman in seconds. Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: • The Burrito Optimization Game and mathematical optimization use cases [03:36] • Key differences between machine learning and mathematical optimization [05:45] • How mathematical optimization is ideal for real-world constraints [13:50] • Gurobi’s APIs and the ease of integrating them [21:33] • How LLMs like GPT-4 can help with optimization problems [39:39] • Why integer variables are so complex to model [01:02:37] • NP-hard problems [01:11:01] • The history of optimization and its early applications [01:26:23] Additional materials: www.superdatascience.com/813

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