Professional Certificate in AI &
Machine Learning Basics
AI and Machine Learning Begins With Me: 4 Aug, 5 Aug 2022 | 9am-5.30pm | Online
Octave Programming For AI, Machine Learning and Data Analytics: 12 Aug 2022 | 9am-5.30pm | Online
Applied Artificial Intelligence: 18 Aug, 19 Aug 2022 | 9am-5.30pm | Online
Introduction to Rapidminer: 26 Aug 2022 | 9am-5.30pm | Online
Duration: 6 Days
This Professional Certificate will equip learners with the following competencies:
- Articulating AI and Machine Learning definitions, approaches, and applications.
- Understanding AI’s advantages, constraints, and the future.
- Having basic skills in Octave programming to model the simple AI modules.
- Understanding basic AI techniques to handle real-world problems.
Learning basic skills to use Rapidminer in the machine learning area.
Job Role Readiness
It will prepare learners in the following job roles to perform their responsibilities more effectively/ It will prepare learners for the following job roles:
- AI and Machine Learning apprentice
- AI and Machine Learning executives
- Business transformers
- Industry transformers
- Business advisors/analysts
Who Should Attend
- Almost all the businesses and industries that would like to employ AI and Machine Learning professionals.
- All the businesses and industries that are going to transform their business model into an AI-driven one.
(Click their photos to view their short biographies)
Dr Ai Xin
Dr Ai Xin is currently a Lecturer with the School of Computing at the National University of Singapore (NUS). She has many years’ experience on teaching Artificial Intelligence and Data Science courses, e.g. machine learning, deep learning, data mining and etc.
She graduated from NUS with a PhD degree on Electrical and Computer Engineering. Her research focused on Game Theoretical Modelling, Optimization Methods, Algorithm Design and Wireless Networks.
She worked in BHP Billiton Marketing Asia for eight years and gained a lot of industry experience through different functions, e.g. risk management, supply chain management, sales and marketing planning and etc.
Dr Amirhassan Monajemi
Dr Amirhassan Monajemi is a Senior Lecturer in AI and Machine Learning with the School of Computing (SoC) at the National University of Singapore (NUS). Prior to SoC, he was a Senior Lecturer in NUS School of Continuing and Lifelong Education (SCALE) teaching AI and Data Science to adult learners. Before joining the NUS, he was with the Faculty of Computer Engineering, University of Isfahan, Iran, where he was serving as a professor of AI, Machine Learning, and Data Science. He was born in Isfahan, Iran. He studied towards BSc and MSc in Computer Engineering at Isfahan University of Technology (IUT), and Shiraz University respectively. He got his PhD in computer engineering, pattern recognition and image processing, from the University of Bristol, Bristol, England, in 2005. His research interests include AI, Machine Learning, Machine Vision, IoT, Data Science, and their applications.
He has taught the artificial intelligence courses, including AI, Advanced AI, Expert Systems, Decision Support Systems, Neural Networks, and Cognitive Science since 2005 at both undergraduate and postgraduate levels. He was awarded the best university teacher of the province in 2012. He also has studied Learning Management Systems, E-Learning, and E-Learning for workplaces since 2007.
Dr Monajemi has registered a few patents in the fields of AI, Machine Vision, and Signal Processing applications, including an AI and machine vision-based driver drowsiness detection system and a low power consuming spherical robot. He also has published more than a hundred research papers in peer-reviewed, indexed journals and international conferences (IEEE, Elsevier, Springer, and so on), and supervised several Data Science, IoT, and AI industrial projects in various scales, including Isfahan intelligent traffic system delivery and testing, and red light runners detection. He is experienced in different sub-domains of Artificial Intelligence and Machine Learning, from theory to practice, including Deep Learning, Logic, and Optimisation.