Dates:
Introduction to Rapidminer:
13 Oct 2023 | 9am-5.30pm | Online
Business Applications Relying on Supervised Learning:
30 Oct, 31 Oct 2023 | 9am – 5.30pm | Classroom Learning
Business Applications Relying on Unsupervised & Reinforcement Learning:
29 Nov, 30 Nov, 1 Dec 2023 | 9am – 5.30pm | Classroom Learning
Duration: 6 Days
Course Objectives
This Professional Certificate will equip learners with the following competencies:
- Design and implement machine learning systems using RapidMiner and to evaluate and test these systems.
- Obtain in-depth knowledge on machine learning techniques, including supervised learning, unsupervised learning and reinforcement learning.
- Apply machine learning techniques to solve real-life business problems.
Job Role Readiness
This Professional Certificate will prepare learners in the following job roles to perform their responsibilities more effectively:
- Data Analysts
- Business Analysts
Who Should Attend
The PC is designed to meet the needs of:
- Executives, Developers, Designers and Managers in Information Technology related fields, business development, strategic planning and operations or roles that require solving data-related problems
Prerequisites
At least a polytechnic diploma
Course Conveners
(Click their photos to view their short biographies)
Dr Amirhassan Monajemi
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 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.
Dr Natarajan Prabhu
Dr Natarajan Prabhu
Dr Natarajan Prabhu is currently a lecturer in the School of Computing at the National University of Singapore. He has 10+ years of experience in teaching for master’s degree programs, undergraduate modules, and continuing education courses. Before joining NUS, he was teaching at DigiPen Institute of Technology, where he taught AI for Games, Digital Image Processing, Machine Learning, Deep Learning, Data Structures, etc. In DigiPen, he developed a master’ degree program for Computer Vision that primarily prepares graduate students to work in the CV industry. After joining NUS as a lecturer, he is currently working on developing and teaching an AI module for non-CS students in Blended learning.
He graduated with a Ph.D. degree from NUS in 2013, a master’s degree, and a bachelor’s degree from Anna University in 2008 and 2006, respectively. His Ph.D. thesis was about automatically controlling and coordinating multiple active cameras in surveillance networks. During this time he has gained rich experience in building multi-camera surveillance systems. He has received “Best PhD Forum Paper” award from International Conference on Distributed Smart Cameras (Hong Kong, 2012) and “Research Achievement Award” from School of Computing, NUS (2012).
Mr Mario Favaits
Mr Mario Favaits
Over the past 25 years, Mario held several sales and operations (P&L) leadership positions at various multinationals across different industries including Enterprise, Automotive, Public Transport, and Software. Currently, he serves as the Executive Director of Services Sales APJ at Crayon, a fast-growing, listed, Oslo-based IT powerhouse. Crayon helps customers to innovate with scalable AI.
Prior to joining Crayon, Mario held leadership positions at Alstom, Continental, Siemens, Oracle, and SMRT, Singapore’s largest Public Transport Operator. Between 2014 and 2019, he was a member of SMRT’s executive leadership team. In 2019, Mario joined the AI and IoT startup scene as an advisor and/or investor.
Mario is the co-chair of EuroCham’s Digital Economy Committee and is the author of an online AI course for Executives, in collaboration with Deloitte.
He holds a Master of Science in Mechanical and Electrical Engineering from the University of Brussels, an MBA from the University of Antwerp Management School, and a Master of Laws from the University of Liverpool.
Course Fees
Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes
Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding.
To enquire, email soc-ace@nus.edu.sg
To register, click Register
Course Codes
Introduction to Rapidminer: TGS- TGS-2022012249 (Classroom Learning) / TGS-2022012285 (Synchronous e-learning)
Business Applications relying on Supervised Learning: TGS-2022015670 (Classroom Learning) / TGS-2022016044 (Synchronous e-learning)
Business Applications relying on Unsupervised & Reinforcement Learning: TGS-2022015668 (Classroom Learning) / TGS-2022015677 (Synchronous e-learning)