Youth Programme: Road to Large Language Models: Basics of Machine Learning in Language Processing

COURSE OVERVIEW

Natural Language Processing (NLP) is a major application area of Machine Learning (ML), a data-driven subfield of artificial intelligence (AI),  commonly used for tasks that include text classification, translation, summarizing, answering questions, amongst others.

Participants will learn about the progression from classical to modern model architectures for NLP applications, including recurrent neural networks and the transformer that powers recent large language models (LLMs) such as OpenAI’s ChatGPT, Google’s Gemini. Through guided hands-on exercises, participants will apply these concepts to modify an implimentation of a simple neural network model for a basic NLP task.

The programme will also examine the ethical and societal implications of generative AI, including impact in fields such as education, how best to integrate AI in our lives, and its limitations in areas such as “understanding” human language, fostering awareness of the challenges and responsibilities in developing and using such systems.

At the end of the programme, participants will be able to:

  • Explain how machine learning techniques are applied to natural language processing tasks;
  • Describe and compare key model architectures used in NLP, including recurrent networks and transformers;
  • Apply basic machine learning methods to process text data and modify the implementation of a simple neural network for NLP task (e.g. sentiment analysis);
  • Discuss the ethical and social implication of AI, including bias, hallucination, and responsible use of AI.

PROGRAMME HIGHLIGHTS

This course is taught by our seasoned NUS faculty – Dr Edmund Low from NUS College, who has conducted more than 10 youth programmes since 2020. In the recent Winter programme, he received a teaching effectiveness rating of 4.71 out 5, one of the highest rated instructors.

The programme does not only focus on the technical side – how to build and train an AI model, but also touches on the impact of AI on our society.

Through this programme, we hope to inspire our youth to take up STEM related subjects, especially Computing and AI in higher education, and rethink how AI could potentially benefit their future career choices and personal development

Who Should Attend

Undergraduate students with an interest in Artificial intelligence and its real-world application.

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 pre-requisites to attend this programme. However, some familiarity with programming (in any language) would be helpful. Some prior exposure to calculus, linear algebra, and statistics would be helpful as well.

Participants are required to bring their own laptop, charger and universal travel adaptor (for international participants) throughout the duration of the programme.

Programme Schedule

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
15 hours of classroom teaching

Mode of Assessment

Final Assessment: Group Project

Frequently Asked Questions

Q: Does student need to learn programming on their own before the programme?
A: No need, we will go through an intro on the specific code syntax used during the course on the first day. 

Q: What’s the tool/platform used during programme? Is there any requirement on laptop specs
A: The platform we are going to use is Kaggle notebooks (hosted on the cloud), so there’s no requirement for laptop specs, as long as it can run basic applications such as MS Office, launch a web browser.

Participants are encouraged to install Chrome on their laptop for both Canvas (NUS’s Learning Management Platform) and Kaggle.

Q: What’s the estimated amount of self-study/preparation time outside the classroom?
A: Around 1 – 2 hours each day, which includes reviewing the material covered in class, and preparing for the presentation. 

Speaker

Associate Prof Edmund Low, NUS College

A/P Edmund Low is an Associate Professor 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.

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.

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

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SPEAK TO US

For enquiries, contact us through the following forms:

  • 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.
 
20 March 2026