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Short Course

Large Language Models Overview and Applications

Programme ScheduleDuration (days)Mode of DeliveryProgramme Code
26 Aug, 27 Aug 2024 | 9am-5.30pm2Classroom LearningTGS-2024044131

Course Overview

This course is designed to empower learners with a good understanding of Large Language Models (LLMs) and their practical applications in modern work environments. Participants will gain hands-on experience and comprehensive insights into popular LLMs like ChatGPT/GPT4.0, as well as other models such as LlaMa2, focusing on their architecture, key functionalities, and applications.

This course targets job productivity improvement and career advancement by equipping participants with essential Artificial Intelligence (AI) skills, meeting industry demands in technology-driven sectors.

Course Objectives

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

  • Understand Large Language Models (LLMs) focusing on Artificial Intelligence (AI) architecture and key features
  • Learn about advanced prompt engineering techniques and how to configure LLMs
  • Use LLMs to enhance productivity in analysis, idea generation, and problem-solving
  • Employ LLMs in various roles like automation, optimizing workflows and research, and bridging skills in learning, analysis, and innovation

Prerequisites

Minimum diploma with basic ICT and programming knowledge.

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This programme is part of

  • Professional Certificate in Enterprise Deployments and Applications for Large Language Models

    • Types of Available Fundings

  • SkillsFuture Credit

    • Instructors


    Dr Yeo Wee Kiang

    Dr Yeo Wee Kiang

    Dr Yeo Wee Kiang is a Senior Lecturer at the Department of Information Systems and Analytics in the NUS School of Computing, where he is dedicated to researching the use of technologies like Adaptive Learning and Large Language Models (LLMs) to create personalized and engaging computing education experiences. With a PhD from NUS focused on machine learning and data mining for pharmaceutical drug discovery, Dr Yeo brings practical industry experience as the former Director of Data Science at a food tech startup, where he led a team applying AI and machine learning to enhance nutritional value. His diverse background includes being an Adjunct Lecturer in the Department of Economics at NUS Faculty of Arts and Social Sciences, helming the Big Data Analytics module in the Master of Economics programme from 2019 to 2023, and teaching Python programming and data analytics at Nanyang Business School. Prior to his academic appointments, Dr Yeo was the Lead Instructor for Data Science at a New York-based commercial Continuing Education and Training (CET) provider, teaching aspiring adult learners in Hong Kong, Sydney, and Singapore. He also holds the WSQ Advanced Certificate in Training and Assessment (ACTA) from the Institute for Adult Learning Singapore. As part of the NUS ACE (Advanced Computing for Executives) teaching team, Dr Yeo specializes in technical training and practical computing education focused on data analytics, machine learning, and Python, aiming to equip professionals with in-demand Data Science skills.

    Programme Fees


    Singapore Citizens Singapore PRs Enhanced Training Support for SMEs International Participants
    39 years old
    or younger    		 40 years old
    or older
    Full Programme Fee S$1,700.00 S$1,700.00 S$1,700.00 S$1,700.00 S$1,700.00
    Less: SSG Grant Amount S$1,190.00 S$1,190.00 S$1,190.00 S$1,190.00 -
    Nett Programme Fee S$510.00 S$510.00 S$510.00 S$510.00 S$1,700.00
    9% GST on Nett Programme Fee S$45.90 S$45.90 S$45.90 S$45.90 S$153.00
    Total Nett Programme Fee Payable, Including GST S$555.90 S$555.90 S$555.90 S$555.90 S$1,853.00
    Less Additional Funding - S$340.00 - S$340.00 -
    Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes S$555.90 S$215.90 S$555.90 S$215.90 S$1,853.00
    1. Total Nett Programme Fee Payable, including GST, after additional funding from various funding schemes.
    2. GST shall apply at prevailing rates.
    3. Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding.
    4. Singaporeans and Singapore PRs are eligible for up to 90% SkillsFuture Singapore (SSG) funding, subject to SSG funding guidelines.

    Advanced Computing for Executives

    11 Research Link
    COM 3, #B1-07
    Singapore 119391
    Telephone: +65 6516 5523
    Email: soc-ace@nus.edu.sg

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