- January 2019 – May 2019: Digital World @ SUTD
During this computer science class, we provide an introduction to Python programming, to students who have never done any programming before. An active learning approach is considered, where students have to study materials and quizzes on a weekly basis, before coming to class. Lectures and exercise sessions are provided, and weekly lab sessions are used to introduce notions of robotics, data science and machine learning. Finally, the students will have to conduct a semester-long project on a topic of their choice, involving machine learning, databases and robotics/sensors.
More info: https://acad.sutd.edu.sg/10-009/
- January 2018 – May 2018: Optimization and ODEs for retakers @ SUTD
During this class, a small number of retakers were given a third chance to pass the Modelling the Systems World class. We first went through the class materials again, at a slow pace, by focusing specifically on being as clear as possible for the students having difficulties. Multiple practice sessions and exam rehearsals were then given, before the students got a second chance at taking the final exam.
- August 2017: Optimization Bootcamp @ SUTD
In this two weeks bootcamp, retakers were given a second chance to pass the Modelling the Systems World class. We first went through the class materials again, by focusing specifically on being as clear as possible for the students having difficulties. Multiple practice sessions and exam rehearsals were then given, before the students got a second chance at taking the final exam.
- May 2017 – August 2017: Advanced Mathematics 1 @ SUTD
The main objective of this mathematics class was to provide firm foundations of single variable calculus. It aims to motivate students on why math is important and to demonstrate mathematics in action. Students learn the basic concepts, techniques, and applications of two branches of calculus – differentiation and integration.
- January 2017 – May 2017: Modelling the Systems World @ SUTD
This mathematics class was divided into two parts – Systems Modelling and Systems Optimization. Systems Modelling introduced the basics of mathematical modeling. Students also learned how to solve ordinary differential equations (first and second order) and the Laplace Transform method. Systems Optimization introduced students to mathematical tools for constrained/unconstrained optimization, convex optimization, numerical solution algorithms, and networks. Throughout the course, several applications that require modeling of real-world systems were discussed.