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Georgia Institute Of Technology ISYE 6501 Module 15 Reference Material - Introduction To Analytics Modeling - GTX ISYE 6501

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ISYE 6501 Module 15 Reference Material Slide 1 - Lesson 15.1 (M): Introduction to Optimization In quite a few of the lesson in this class, we've seen various models that I've noted can be solved us ... ing optimization. You've probably heard me refer to optimization in lessons on classification. Clustering, regression, variable selection, time series analysis and more. The reason is that many, perhaps most, statistical and machine learning models involve optimization to find their answers. So optimization is a key underlying part of descriptive and predictive analytics, as we'll see a little bit more formally in this topic. But optimization is also a very important topic in analytics in its own right, especially for prescriptive analytics. The type of analytics that asks, given what I know and what I predict, what's the best course of action to take? Optimization can answer questions like, which airplane mechanic should be scheduled for each shift over the next week? To meet the expected maintenance requirements at O'Hare Airport at lowest cost. Making sure we don't violate any federal regulations or union contract requirements and accounting for unexpected breakdowns. How much crude oil should be send by tanker and by pipeline from each oil field to each refinery to meet demand and avoid oversupply while keeping costs low? What webpages should be optimized in a server farm? And how many copies of each should be stored to maximize the profit made by responding quickly to hits? How should a large machine shop sequence its production to get maximum throughput while meeting all clients' deadlines? Taking into account the reality that some output will fail inspection and need to be remained. [Show More]

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