Learn how to design and implement a small-scale artificial neural network (ANN) and mimic the learning process of the human brain.
In this free online event with ARU's Dr Mahdi Maktabdar Oghaz, we'll use Python programming language and deep learning libraries to implement a number of real-world applications of ANN such as object detection and human detection.
ANN is the cutting edge of artificial intelligence, inspired by the structure and function of the biological neural networks in the human brain. ANNs are used to learn patterns in data and make predictions or decisions based on that data.
The majority of today’s AI technologies such as autonomous vehicles, smart medical diagnosis, natural language processing, and image and video recognition are relying on this powerful tool.
This event is part of our Computer Science Master Class series running in June 2023.
Dr Mahdi Maktabdar Oghaz obtained his PhD in computer science from University Technology Malaysia (UTM) in 2016. His primary research focus during his doctoral studies was computer vision and machine learning in specific accurate skin detection for medical applications.
Right after his PhD, Mahdi started his career as a postdoctoral researcher at UTM, working on a research project sponsored by Cyber Security Malaysia and the Ministry of Higher Education Malaysia, aimed at promoting safety and security in cyberspace using artificial intelligence and machine learning techniques.
In 2018, he joined Kingston University London's ROVIT research team to work on the H2020 MONICA project, which aimed to promote crowd safety and security in large-scale outdoor events using video analytics, artificial intelligence, and computer vision techniques. In 2019, he progressed his career to Lecturer at ARU's School of Computing and Information Science.
As a result of his research career, Mahdi managed to publish several articles in various international journals and conferences. His primary research area includes deep learning and convolutional neural networks, machine learning, crowd analysis, and medical image processing.
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