Expecting the Unexpected from Machine Learning: Facts and Fallacies
In recent times, we have witnessed an exponential growth in applying machine learning principles for diverse purposes. One of the most popular definitions of machine learning (ML) is given by “Machine Learning is part of research on artificial intelligence aiming at getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding data and information in the form of observations and real-world interactions”.
In a recent study by McKinsey, ML was identified as one of 12 technology areas with high impact on how people live and work and on industries and economies. Human activities produce a substantial amount of digital data which needs to be processed and understood. ML techniques provide the tools for such an analysis for different purposes including classification, prediction, regression etc…
In this presentation, Machine Learning is defined a bit in more details We will go through the developments of this rapidly emerging field, the different types of Machine Learning algorithms are explained and examples from different application areas are discussed. We will show how computers are able (or will be able) to solve problems that were dependent upon human expertise for a long time. We will discuss which areas of application pose the greatest challenge to ML technology and we will give an insight into what is expected from ML in the future.
Date and Time
Location
Hosts
Registration
- Date: 20 Nov 2018
- Time: 07:00 PM to 08:30 PM
- All times are (UTC+03:00) Riyadh
-
Add Event to Calendar
- Aloft Dhahran Hotel
- Level 1
- Dhahran, Al Mintaqah ash Sharqiyah
- Saudi Arabia 31261
- Room Number: Tactic 3
- Contact Event Host
-
Dr. Mohammad AlMuhaini
muhaini@ieee.org
- Starts 08 November 2018 02:43 PM
- Ends 20 November 2018 07:00 PM
- All times are (UTC+03:00) Riyadh
- 0 in-person spaces left!
- No Admission Charge
Speakers
Dr. Mohamed Deriche of KFUPM
Biography:
Dr Mohamed Deriche received his PhD in 1994 from University of Minnesota, USA. He joined the Queensland University of Technology, Australia, in 1994. In 2002, he joined the EE Department at King Fahd University of Petroleum & Minerals, KFUPM, where he is leading the signal processing group. He has published over 250 papers in multimedia signal and image processing and holds several patents. He has delivered numerous invited and tutorial talks. He has chaired several conferences including TENCON, GLOBALSIP-MPSP, IEEE GCC, and IPTA. He has supervised more than 35 MSc and PhD students. He has received the ENP best student award, the IEEE third Millennium Medal, the Shauman award from best researcher in the Arab world. He received two times the excellence in research award from KFUPM and the excellence in teaching award. He is the recipient of 4 best papers awards. His research interests include Multimedia signal and image processing, Quality of Experience, Seismic data analysis, Biometrics, and Biomedical applications.
Email:
Agenda
- Defining machine learning (ML)
- Machine versus artificial intelligence
- Broad classes of machine learning techniques
- Machine learning and deep learning
- Application of machine leaning
- Glimpse form the future