Machine Learning in (I)IoT
The past few years has seen a significant resurgence in interest and application of Artificial Intelligence and Machine Learning. This has been primarily driven by the ready availability of relatively high and scalable compute power, cloud systems and virtualisation. It has also occurred coincident with emerging interest in Big Data and (Industrial) Internet of Things.
Machine Learning application at this time is dominated by statistical techniques such as Bayesian Inference. This, and other similar statistical approaches are well suited to the large datasets as are often seen within the (I)IoT domain. However, they are not the only Machine Learning techniques that are currently available which are also applicable to (I)IoT.
This presentation covers a number of semi- and non-statistical Machine Learning techniques that may be applied in various (I)IoT contexts. The presentation is tutorial in nature, focuses primarily on the Machine Learning techniques themselves, as well as typical ways that they can be applied to (I)IoT.
The overall aim is to give (I)IoT practitioners a (non-mathematical) overview and appreciation of a number of additional Machine Learning techniques that can complement statistical methods, and may find application in their day-to-day work.
About the speaker
John Ypsilantis holds degrees in Electrical Engineering, Computer Science and Pure Mathematics from the University of Sydney. His doctoral studies in Electrical Engineering at the University of Sydney concerned the application of machine learning to SCADA Systems for power, and were conducted during the third wave of interest in artificial intelligence. He has over 30 years experience in the field of engineering-oriented AI and Machine Learning.
He is the Principal of Heuristics Australia Pty Ltd, an electrical engineering and ICT consultancy based in Sydney, which works with large public and private utilities in Australia and overseas. John’s other specialisations include automatic control, data and voice communications systems, SCADA for gas, water and electricity utilities, Industrial Internet of Things, network security for utilities and the application of machine learning and intelligent systems to utility operations.
John is a Member of Engineers Australia, Chartered Professional Engineer and registered on the NER and RPEQ.