IEEE Computer Society. Recap: Machine Learning (Presentation held at DeVry JUNE 6 2017)
|September 13, 2017||Posted by CSauthor2 under CS||
Dr. Brad Morantz was kind enough to make his return appearance to Foothill Section (from his home Phoenix IEEE Section) to give us another technology update on a really timely topic: Machine Learning. With so much technology in the news that depends on enhanced machine learning, it was important to give our members and guests a better understanding from a real expert in this field. Dr. Morantz has spent most of his career in exactly this area and gave us an education on the history and progress in this field.
As you will see when you review his slide set, he provides numerous examples to help illustrate his key points; along with some interesting graphics. The key areas he covered were: Data mining, various clustering methods, self organizing maps, model building, decision trees, Bayesian learning, and supervised vs. unsupervised training. He provided many additional references for those who want to proceed further into this subject matter.
As is usual with our Computer Society meetings, Dr. Morantz took many questions from the floor until our time was exhausted. He stayed around to meet and discuss more issues with interested parties. Foothill Section is to be thanked for providing plenty of great pizza and sodas to keep the crowd satisfied and engaged.
We always enjoy his visits to Foothill Section, and will look forward to a future presentation from him.
DETAILS: For more info and slide presentation, see original meeting announcement post (further down this webpage).
IEEE Computer Society. Machine Learning Presentation (DeVry JUNE 6 2017)
Topic: Machine Learning
Speaker: Brad Morantz, Ph.D.,
IEEE Computer Society Phoenix Chapter
“Machine learning” is a very hot topic. But what does it mean? What is it really? How is it accomplished? How does it affect us? Did we enroll R2D2 in college? This presentation will define machine learning and explain what it means and how it is applied in both the research and the applied practitioner worlds.
This subject has been given as a one or two semester course at various schools and universities. It could be far more than this because of the basic statistics and underlying mathematics. This lecture will cover the basic principles and definitions of the various aspects of machine learning. Explanation of each current type will be discussed. Some methods and example applications will be covered. Some of the subjects that will be explained are: Data mining, various clustering methods, self organizing maps, model building, decision trees, Bayesian learning, and supervised vs. unsupervised training. These will be explained relative to the definitions given.
About the Speaker
Dr. Morantz has a B.S. in C.I.S. and E.E., a M.S. and Ph.D. in Decision Science, a mixture of mathematical science, psychology, and computer science. He has additional post doctoral course work in Computational BioScience, Computer Science, statistical design methodology, and Design Analysis Simulation Experiments (DASE). Dr. Morantz has published and presented on neural networks, multiprocessing mathematics, biologically inspired computing architecture, data-mining, and intelligent decision making. His current research is in biologically inspired computing for intelligent decision making. He is currently Sr Staff Scientist for Bluemont Technology & Research, Inc of McLean VA. He is also on the editorial board of the International Journal of Data Mining, Modeling, and Management. Regarding IEEE, he is a senior IEEE member and Vice Chair of the Phoenix IEEE Computer Society. He is also a member of the IEEE Computational Intelligence Society and his website is www.machine-cognition.com