Abstract: Want to learn more about trends like AI, IoT and wearable tech? In this workshop, we will cut through the hype by building a “smart” fitness tracker using your own mobile device. We’ll do hands-on exercises: you’ll acquire data from sensors, design a step counter and train a human activity classifier. You will leave motivated and ready to use machine learning and sensors in your own projects!
The goal for this session is twofold: show our participants how easy it is to get started with Machine Learning and wearables and set them up for success to take on challenging projects in this domain. A clear instruction sheet will guide the participants through exercises ranging from beginner to intermediate level. This workshop is highly interactive with hands-on exercises in which the participants will create a “smart” wearable device and test it out. We aim to demonstrate how powerful applications can be built when we combine machine learning, sensor analytics, and IoT. Participants will use the free MATLAB Mobile App to access built-in sensors and analyze data on their mobile devices.
● MATLAB Online;
● MATLAB Mobile;
● MATLAB Drive;
● mobile devices such as a phone or tablet;
Bio: Maria Gavilan-Alfonso is a Senior Online Content Developer at MathWorks, where she helps integrating MATLAB and Simulink in online learning experiences worldwide. She is one of the developers and instructors of the Coursera specialization “Practical Data Science with MATLAB.” Maria holds a MSc in Aerospace Engineering from Purdue University, a BSc in Physics from Universidad Nacional de Colombia, and she’s currently an MBA candidate from University of Illinois at Urbana-Champaign. In her professional career Maria has supported and trained engineers using simulation software for aerodynamics, aeroacoustics, thermal management and flight dynamics. Her current interests are focused on autonomous aerial vehicles and machine learning techniques applied to aerodynamics. Maria also volunteers in multiple events looking to increase the representation of women in science and engineering.