#MathematicalOptimization #OptimizationProblems #DataDrivenDecisionMaking Jerry Yurchisin from Gurobi joins @JonKrohnLearns 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 natalie@superdatascience.com for sponsorship information. In this episode you will learn: • [00:00:00] Introduction • [00:02:06] The Burrito Optimization Game and mathematical optimization use cases • [00:04:15] Key differences between machine learning and mathematical optimization • [00:12:20] How mathematical optimization is ideal for real-world constraints • [00:20:03] Gurobi’s APIs and the ease of integrating them • [00:38:07] How LLMs like GPT-4 can help with optimization problems • [01:01:05] Why integer variables are so complex to model • [01:09:31] NP-hard problems • [01:24:51] The history of optimization and its early applications Additional materials: https://www.superdatascience.com/813