Short Course: A Primer on Machine Learning for Engineers and Managers
Description of Course
This tutorial provides an introduction to the principles and applications of machine learning algorithms, software and applications. The tutorial begins with an introduction to the basics of pattern matching, feature extraction, and supervised and unsupervised learning. The tutorial then covers basic methods such as the k-means, support vector machines, neural nets and deep learning. The coverage is at a t high level for beginners featuring functional block diagrams, qualitative descriptions, and software examples. The course connects algorithms with sensor applications including health monitoring, IoT, and security applications.
Topics:
Qualitative Overview, what is machine learning?, Use in Sensors and Big Data, Algorithms and Software, Begings from Vector Quantization and Cell Phones, Feature Extraction, K-means, Adaptive Neural Nets, Support Vector Machines, Bayesian Methods, Deep Learning, Embedding machine learning on sensor boards, Applications; IoT, health monitoring, security; smart campus, smart cities; social implications.
Who Should Attend
The tutorial is designed for students, engineers and managers who need to understand the basics of machine learning and their utility in various sensor applications. The tutorial should be of particular interest to engineers and managers who need to prepare for projects that involve learning algorithms for sensors.
Organized by ASU and ITESM
In Collaboration with the MEMS & Sensors Industry Group
Sponsored in part by NSF International Programs, the NSF I/UCRC program and the ASU SenSIP Center.
Technical Co-Sponsor: IEEE Phoenix SPCOM Chapter
https://sensmach.asu.edu 