Machine learning for everyone | Around Campus - MIT News

A new EECS course on applications of machine learning teaches students from a variety of disciplines about one of today’s hottest topics, inform Emily Makowski, School of Engineering.

Mingman Zhao, a PhD student in EECS, spoke to the inaugural 6.883/6.S083 class about common issues in using machine learning tools to address problems.
Photo: Lillie Paquette/School of Engineering

A graduate student researching red blood cell production, another studying alternative aviation fuels, and an MBA candidate: What do they have in common? They all enrolled in 6.883/6.S083 (Modeling with Machine Learning: From Algorithms to Applications) in spring 2019. The class, offered for the first time during that term, focused on machine learning applications in engineering and the sciences, attracting students from fields ranging from biology to business to architecture.

Among them was Thalita Berpan, who was in her last term before graduating from the MIT Sloan School of Management in June. Berpan previously worked in asset management, where she observed how financial companies increasingly focus on machine learning and related technologies. “I wanted to come to business school to dive into emerging technology and get exposure to all of it,” says Berpan, who has also taken courses on blockchain and robotics. “I thought; ‘Why not take the class so I can understand the building blocks?’”...

The class includes live lectures that focus on modeling and online materials for building a shared background in machine learning methods, including tutorials for students who have less prior exposure to the subject. “We wanted to help students learn how to model and predict, and understand when they succeeded — skills that are increasingly needed across the Institute,” says Jaakkola, the Thomas Siebel Professor in EECS and the Institute for Data, Systems, and Society (IDSS).
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Source: MIT News