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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\u2019s latest CPUs are speeding up solutions to problems like the Traveling Salesman in seconds.\n\nInterested in sponsoring a SuperDataScience Podcast episode? Email\xa0natalie@superdatascience.com\xa0for sponsorship information.\n\nIn this episode you will learn:\n\u2022 The Burrito Optimization Game and mathematical optimization use cases [03:36]\n\u2022 Key differences between machine learning and mathematical optimization [05:45]\n\u2022 How mathematical optimization is ideal for real-world constraints [13:50]\n\u2022 Gurobi\u2019s APIs and the ease of integrating them [21:33]\n\u2022 How LLMs like GPT-4 can help with optimization problems [39:39]\n\u2022 Why integer variables are so complex to model [01:02:37]\n\u2022 NP-hard problems [01:11:01]\n\u2022 The history of optimization and its early applications [01:26:23]\n\nAdditional materials:\xa0www.superdatascience.com/813