Abstract: 

The proliferation of sensor technologies has resulted in more connected machines than ever before. This change is resulting in huge quantities of sensor data becoming available for analysis. Machine learning algorithms have resulted in a mixed track record of success with these data sources. This talk will give an overview of the state of machine learning as applied to IoT and industrial equipment. It will discuss some of the challenges with current approaches, exciting theoretical advancements and some ""lessons learned"" from the field.

Specifically:
What do we mean by IoT?
What is failure prediction and prognostics?
What is the value of IoT?
Differences between physics based approaches to IoT and data-driven approaches to IoT
What are the challenges from applying data-driven approaches to IoT?
How can recent advances in machine learning help with the unique challenges of IoT?
Real-case study that illustrates the application of deep-learning, gradient boosting, transfer learning and other machine learning techniques for IoT applications
What are the opportunities for future enhancements and exciting research in this area?

Bio: 

Adam McElhinney is currently the Head of Data Science at Uptake Technologies, where he leads a team of 75 Data Scientists building cutting-edge industrial data analytics tools. Additionally, Adam is an Adjunct Professor in the Computer Science and Mathematics departments at Illinois Institute of Technology. Additionally, Adam has filed 18 patents for his research in machine learning, internet of things (IOT), software engineering and big data technology. Adam was recognized by the Illinois Technology Association as the 2018 Technologist of the Year.