ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING (DATA SCIENCE)
The programme introduces participants to the latest technologies in artificial intelligence and machine learning. At the completion of this course, students will have the knowledge on the fundamentals of AI including the various types of machine learning algorithms. This course also aims to equip students with skills and hands-on experience in applying AI and ML on real-world data using the Internet-of-Things as an example application.
- Thematic discussions, live tutorials and short practice sessions from NUS faculty and teaching assistants.
- The live tutorials will cover hands-on exercises and in-depth discussions through interactive breakout rooms.
- The programme is designed with Continuous Assessments format to sharpen the understanding and application of ideas learned during the lectures.
- Mini Project – There will be a mini group project in which students will solve and analyse a problem related to AI and Machine Learning.
| Welcome message from Dean of NUS SCALE
||Tutorials conducted over Zoom
There are no specific pre-requisites in terms of math or computational science. However, familiarity with scientific programming languages such as Python would be helpful. The class will incorporate practical assignments in Python. Also, exposure to calculus, linear algebra, and statistics would be helpful.
WHO SHOULD ATTEND
University students majoring in Science, Technology, Engineering, Computing, and Mathematics subjects. Professionals looking to understand how AL/ML work and how they apply it in their workplace.
MODE OF DELIVERY
20 hours of hybrid online programme, including asynchronous lessons, tutorials and a final project presentation.
MODE OF ASSESSMENT
- Group project
Dr Mehul Motani, Associate Professor, Department of Electrical and Computer Engineering,
Faculty of Engineering
Prof Mehul Motani received the B.E. degree from Cooper Union, New York, NY, the M.S. degree from Syracuse University, Syracuse, NY, and the Ph.D. degree from Cornell University, Ithaca, NY, all in Electrical and Computer Engineering.
Prof Motani is currently an Associate Professor in the Electrical and Computer Engineering Department at the National University of Singapore (NUS) and a Visiting Research Collaborator at Princeton University, USA. He is also a member of the NUS Institute for Data Science, the N.1 Institute for Health, and the NUS Smart Systems Institute. Previously, he was a Visiting Fellow at Princeton University. He was also a Research Scientist at the Institute for Infocomm Research in Singapore, for three years, and a Systems Engineer at Lockheed Martin in Syracuse, NY for over four years. His research interests include information theory and coding, machine learning, biomedical informatics, wireless and sensor networks, and the Internet-of-Things.
Prof Motani was the recipient of the Intel Foundation Fellowship for his Ph.D. research, the NUS Annual Teaching Excellence Award, the NUS Faculty of Engineering Innovative Teaching Award, and the NUS Faculty of Engineering Teaching Honours List Award. He is a Fellow of the IEEE and has served as the Secretary of the IEEE Information Theory Society Board of Governors. He has served as an Associate Editor for both the IEEE Transactions on Information Theory and the IEEE Transactions on Communications. He has also served on the Organizing and Technical Program Committees of numerous IEEE and ACM conferences.
CERTIFICATE OF COMPLETION
Successful participants who complete all requirements of the programme including passing the assessment will receive a certificate of completion issued by NUS SCALE.
A sample of the certificate of completion can be found here:
Participants from the project team with the highest score for the group project will also receive a commendation letter. A sample is shown below:
14 January to 12 February 2023
in collaboration with Looker Education Group
For programme enquiries, please fill the form here.