AI, Deep Learning, Optimization: How to Frame and Solve Real World Problems Effectively to Get the Most Business Benefits
AI, Deep Learning, and Machine Learning (ML) have been receiving much attention in recent years due to their big potentials to deliver significant business benefits. In this talk, we will discuss the interactions and relationships of AI, deep learning, ML, optimization models, the utilities of each and their and best uses in different business contexts. We will look into how to frame your business problems properly, ways to select the more effective models, and how to implement efficient solutions that highlight and optimize the most critical business tradeoffs in line with your (sometimes subtle and unspoken) business priorities and strategies. We will include examples from various industries for illustration. This presentation can benefit both data scientists and business executives who are interested in leveraging machine learning to gain business advantages for their companies. Takeaways for the audience include:
- better understanding of AI, deep learning, machine learning, optimization modeling concepts, their common roots, and where they can be best applied
- a framework that helps guide the definition, framing and formulation of the business problems to get the model solution that can best support your business priorities
- industry examples that help demonstrate how it's done
Yanqi Xu is co-founder and VP of Data Science & AI of Alps Analytics Group, where he is in charge of researching and developing machine learning and predictive models to create intellectual property and to help companies better leverage their assets to grow revenue, market share and improve profitability. His analytics experience spans several industries, and as part of the data science leadership team, Yanqi has helped companies such as United Airlines, Avis, Princess Cruises and Raytheon (Flight Options) make strides in improving revenue and profits by developing award-winning models in price optimization, machine learning, combinatorial optimization, and customer analytics.