How to think about Machine Learning - Theoretical Foundations for AI Solution Development, Business Engagement, Risk, and Architectural Best Practices.
 

The laws that govern AI are also its properties. In this talk, we will explore the foundations of Machine Intelligence. We will also take a deeper look at some popular methods and algorithms of the big data age and explore their design motivations. This will shed light on the objectives of various activities involved in robust AI solution development.

Sharad Shandilya is a scientist, strategist, and execution lead across digital marketing, Financial Services and Healthcare, and cloud. A published author in digital, IoT, and A.I., he leads A.I. and Big Data for Fidelity Investment's Institutional Business.

Sharad's books, peer-reviewed publications and I.P. span A.I., big data, and real-time technologies. Prior to Fidelity, he established data science practices for Digitas (Publicis.Sapient) and Omnicom Media Group. He has conceptualized and developed platforms, products, operating models, and centers of excellence; enabling exponential growth of startups and A.I. driven digital transformations of large enterprises in the new era of big compute and big data.

His education includes masters and doctorate in computer science; and post doctoral AI technology development at University of Michigan Ann Arbor.