COURSE OBJECTIVES
This programme is specially curated for pre-university and high schoolstudents in Grades 10-12, to introduce Artificial Intelligence and Machine Learning and their applications to the young generation of talents. AI and Machine Learning are the most disruptive technology of the future, since they can dramatically increase the operational performance of different businesses, as well as they explore new areas and will change our society and business models. Therefore, knowing how they work and what they are is a must for the next generation of experts.
Over the course of these lectures, participants will gain an understanding of what AI and Machine Learning actually are, and the basic principles behind them. From these fundamentals, they will then be provided with an overview of the current AI/Machine Learning trends, and their applications in modern businesses.
At the end of the course, the participants will:
- Build a solid foundation to enable participants to further develop their AI skillset over the course of their training and careers.
- Equip themselves with the basic skills and knowledge to allow them to identify opportunities to improve their personal and business performance through AI.
Who Should Attend
Pre-University and high school students in Grade 10 - 12, with an interest in exploring AI and its applications. The course is not restricted to STEM students, as the material will cover the basics of AI/Machine Learning through advanced applications.
Pre-Requisites
Participants are expected to be able to read, write and communicate in English, as the programme will be conducted in English.
There are no subject-matter-specific prerequisites to attend this programme. The minimum age requirement is 15 years old, at the start of the programme.
Programme Schedule
Upcoming Future Series: 15 - 20 December 2024
For individuals interested in NUS SCALE Youth programmes, please click here to enquire.
For schools/companies interested in customised and/or group bookings, please click here.
Mode of Delivery
On campus.
Five 3-hour in-classroom training sessions.
Mode of Assessment
Frequently Asked Questions
Q: Does student need to learn programming on their own before the programme?
A: No need, the tool used for teaching is Orange, a component-based visual programming software package for data visualization, machine learning, data mining, and data analysis. No prior coding knowledge is required.
Q: What’s the tool/platform used during programme?
A: Participants are required to bring their own laptop (WindowsOS/MacOS), and install Orange before coming to the programme. Installation instructions will be provided closer to the programme date. More information could be found: Orange Data Mining - FAQ.
Q: What’s the estimated amount of self-study/preparation time outside the classroom?
A: Around 2 - 3 hours each day, which includes reviewing the material covered in class, and preparing for the presentation.
COURSE INSTRUCTORS
Below are the faculty members who have developed the course, and taught the programme in the past (names are arranged in alphabetical order):
Dr AI Xin
Lecturer, Department of Computer Science
School of Computing
Dr. Ai Xin is currently a Lecturer with the School of Computing at the National University of Singapore (NUS). She has many years’ experiences on teaching Artificial Intelligence and Data Science courses, e.g. machine learning, deep learning, data mining and etc.
She graduated from NUS with a PhD degree on Electrical and Computer Engineering. Her research focused on Game Theoretical Modelling, Optimization Methods, Algorithm Design and Wireless Networks.
She worked in BHP Billiton Marketing Asia for eight years and gained a lot of industry experience through different functions, e.g. risk management, supply chain management, sales and marketing planning and etc.
Dr Edmund Low
Senior Lecturer, NUS College
Dr Edmund Low is a senior lecturer with the NUS College (NUSC) at the National University of Singapore. He has more than 14 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 the use of programming and visual libraries to develop simulation models for automating workflow processes, and the setting up of remote environmental sensing systems to automate real-time continuous monitoring, for early incident warning. He currently heads the quantitative reasoning domain, and is also director of the Quantitative Reasoning Centre, at University Scholars Programme (NUS USP). As an educator, Edmund has received 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.
Dr Prabhu Natarajan
Lecturer, Department of Computer Science
School of Computing
Dr. Prabhu is an experienced lecturer currently teaching at the School of Computing in the National University of Singapore. With over a decade of experience teaching master's degree programs, undergraduate modules, and continuing education courses, he previously taught at DigiPen Institute of Technology. There, he developed a master's degree program for Computer Vision, aimed at preparing graduate students to work in the CV industry, and taught subjects such as AI for Games, Digital Image Processing, Machine Learning, Deep Learning, and Data Structures. Since joining NUS as a lecturer, Dr. Prabhu has been working on developing and teaching an AI module for non-CS students using blended learning techniques. In recognition of his teaching excellence, he was awarded the Faculty Teaching Excellence Award forthe AY2021-22.
Dr. Prabhu received his Ph.D. degree from NUS in 2013, with his thesis focusing on automatically controlling and coordinating multiple active cameras in surveillance networks. His experience in building multi-camera surveillance systems was recognized when he received the "Best PhD Forum Paper" award from the International Conference on Distributed SmartCameras in Hong Kong in 2012, as well as the "Research Achievement Award" from the School of Computing, NUS in the same year. He also holds a master's and a bachelor's degree from Anna University, India.
Certificate of Completion
Successful participants who fulfill all program requirements, including meeting the minimum attendance and passing the assessment, will be awarded an e-Certificate of Completion and Assessment Report issued by NUS SCALE.
Certificate of Completion
A sample of the Certificate of Completion.
Assessment Report
A sample of the Assessment Report.
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Commendation Letter
Participants from the project team with the highest score for the group project will also receive a commendation letter.
Commendation Letter
A sample of Commendation Letter
SPEAK TO US
For enquiries, do contact us through following forms should you have any questions regarding this programme:
For individuals interested in NUS SCALE Youth programmes, please click here to enquire.
For schools/companies interested in customised and/or group bookings, please click here.
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