Dates: 17 Oct, 18 Oct 2023 | 9am-5.30pm | Classroom Learning
Duration: 2 Days
ChatGPT has got a wide range of applications. Also, it is a rather user friendly application. However, the main challenge is when and how to employ it. We are going to show the participants a set of clear hints and practices about the tool, and when and how to use it. Again, this is a way to learn more about the ChatGPT models. This course provides a practical and comprehensive overview of ChatGPT, from subscription to mastering its advanced features. Best practices and hints for using ChatGPT are also included. Also, the ethical issues and the risks concerning ChatGPT would be addressed in this course as well as business robotising using ChatGPT.
This course is part of Professional Certificate in ChatGPT, Advanced Chat Models, and Generative AI.
After attending this course, participants will be able to
• Understand ChatGPT and its major components
• Make decisions using ChatGPT
• Do predictive data analysis using ChatGPT
• Articulate advanced features and capabilities of ChatGPT
• Use ChatGPT as an advisor or robot
• Fine-tune ChatGPT robot for a specific business
• Understand the strengths, weaknesses, risks and limitations of ChatGPT
Minimum of a diploma with basic ICT knowledge
(Click photos to view biographies)
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 Edmund Low
Dr Edmund Low is currently Senior Lecturer with the NUS College at the National University of Singapore.
He has nearly 20 years of academic and professional experience in the use of data-driven tools to answer questions in public health and the environment. His past projects include applying AI techniques and machine learning models for environmental modelling and impact assessment. He currently heads the quantitative reasoning domain at USP, and teaches courses on statistical methods, data science and machine learning. As an educator, Edmund is a multiple recipient of both the USP Teaching Excellence Award, as well as the NUS Annual Teaching Excellence Award. Edmund holds a PhD in Environmental Engineering from Yale University.
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.
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TGS-2023022212 (Classroom Learning)
Course Fee Breakdown
Singapore Citizens39 years old or younger
Singapore Citizen40 years old or older