Solving Real-World Problems with Machine Learning

Presented by

Ashrith Barthur, Sandip Sharma

About this talk

Description: In much of the 21st century, we have seen how machine learning is being used by virtually every fortune 500 company. But do we know how it is used? Is it a tool? Is it a template? Is it design, concept or a way of thinking? Or is it all in one coming together to solve problems in the world - but, one at a time? In this webinar, we showcase how we are solving the problem of identifying false positives in money laundering alerts, and optimizing them with machine learning. But machine learning takes a backseat, although it is the kernel of the entire solution we focus on how a real-world problem, with steps, is solved from end-to-end. Speaker's Bio: Ashrith Barthur: Ashrith Barthur is the security scientist designing anomalous detection algorithms at He recently graduated from the Center of Education and Research in Information Assurance and Security (CERIAS) at Purdue University with a Ph.D. in Information security. He is specialized in anomaly detection on networks under the guidance of Dr. William S. Cleveland. He tries to break into anything that has an operating system, sometimes into things that don’t. He has been christened as “The Only Human Network Packet Sniffer” by his advisors. When he is not working he swims and bikes long distances. Sandip Sharma: Sandip is an entrepreneur and technology leader, who has a balance of work experience in both Financial Services Industry and Government. With +20 years of experience in business IT, Sandip thrives for developing and implementing innovative AI/ML solutions to the Whole-of-Government and Financial Services Industry on emerging digital technologies. He has Masters. Degree in Business IT – Financial Services, from Singapore Management University (SMU).

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