ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING (COMPUTER VISION)

COURSE OBJECTIVES

Machine Learning (ML), a branch of Artificial Intelligence, has proven useful in many fields, such as medical diagnosis, e-commerce, security, education, and so on. This short course introduces the basic technical concepts in ML, especially where it is used in image processing. You will learn enough to implement a simple system that recognizes traffic signs, and be able to further your own in-depth study into this exciting field.

PROGRAMME HIGHLIGHTS

Welcome message from Dean of NUS SCALE	Tutorials conducted over Zoom

PRE-REQUISITES

Knowledge of: Single-variable Calculus, Matrix and Vectors, Probability and Statistics. Prior programming experience in a modern language, eg. Java, Python, C#, C/C++.

WHO SHOULD ATTEND

Undergraduates with programming experience and are interested in exploring machine learning.

MODE OF DELIVERY

23 hours of hybrid online programme, including asynchronous lessons, tutorials and a final project presentation.

MODE OF ASSESSMENT

• Continuous Assessment: Quiz
• Group project: Traffic Sign Recognition

Dr Terence Sim, Associate Professor, NUS School of Computing Prof Terence Sim is an award-winning teacher and researcher with over 20 years of experience at the NUS School of Computing. His research areas include: computer vision, biometrics, and machine learning. He serves as Editor-in-Chief of the International Journal of Pattern Recognition and Artificial Intelligence. Prof. Sim obtained his PhD from Carnegie Mellon University, MSc from Stanford University, and SB from the Massachusetts Institute of Technology.

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: