Professional Certificate in Artificial Intelligence
for Non-AI Scientists
A strong wave can overwhelm you, AI too, but together we can rule this wave and get ready for the AI-driven future.
This professional certificate will address both theoretical and practical aspects of AI to make the subject accessible to learners who do not have deep technical background. The programming skills introduced will provide the tools and framework for the development of data analytics and machine learning programmes widely used in AI. Skills and knowledge in-hand, participants would be better equipped to identify possible applications of AI in their workplace, with the option to use it to meet operational, customer support, or business support challenges.
Course Objectives
To train apprentices that would be able to recognize AI techniques and their applications in tackling real-world problems, as well as being able in AI systems modeling, and development of Octave programs.
Learning Outcomes
At the end of the program, the participants will be able to:
- Understand the now and future of AI, its definitions, approaches, and applications
- Describe the impacts of AI on different businesses and how to implement AI in real-world businesses
- Develop Octave programs
- Model AI systems using Octave programming language
Programme Format
Participants will learn the theory of an AI-related subject, then experiment that personally or a small team. All the practices are job-related and address real-world problems. Course contents are being revised and updated regularly.
Software Application
Window OS is recommended.
Facilitators
(Click their photos to view their short biographies)
Dr Amirhassan Monajemi
Dr Amirhassan Monajemi
Dr Amirhassan Monajemi is a Senior Lecturer in AI and Data Science with the School of Continuing and Lifelong Education (SCALE) at the National University of Singapore (NUS). 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 1968 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.
Insight from Instructor
“We are living in a very rapidly changing world. Disruptive technologies such as AI and Machine Learning are going to change our lifestyle and the way we do the businesses. This professional certificate will help you to understand the basic structure of AI systems and solutions, and how they work practically.”
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