EECS 2017

International Conference on Electrical Engineering and Computer Sciences


Best Paper Award


Keynote Speech

Topics: Machine Learning in Computer Science

Speaker: Dr. Katie Brodhead

Associate Professor

Department of Mathematics, Florida A&M University, USA

Abstract

Evidence suggests that growth in the technology revolution is at an exponential rate and there no evidence of any slowdown in the near term. Much of the success is owed to techniques in machine learning.  Recent developments at Google and at Apple provide windows into this current growth track.  Last year, AlphaGo, developed by Google's DeepMind unit, became the first computer program to beat the top human professional Go player without handicaps.  At Apple, it was only three years ago that the company's flagship interactive human assistant, Siri, used "hidden Markov models" as the default computing system to answer user queries.  However Apple recently moved to machine learning and deep neural network techniques. Some applications include object recognition, face recognition, and scene classification. In this talk we first give examples of these applications and provide an introduction to machine learning.  A few areas of author interest are also provided.  In doing so, the future of artificial intelligence with respect to machine learning is considered.  What might these developments portend for the future of society?  We consider some of these questions and advance the idea of the integration of other sciences into the current landscape of machine learning.

Program

201712 Singapore Conference Program


Member Center

Online Submission

Important Dates

Submission Deadline

October 28, 2019

Notification of Acceptance
From November 19, 2019
Registration & Payment Deadline
December 19, 2019
Conference Date
February 18-20, 2020

Contact & Inquiry
EECS Secretariat

Email: eecs@iceecs.org


Organized by

MOCT Education Institution