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Specialist Diploma in Applied Generative Artificial Intelligence

School of Infocomm, Lifelong learning, Specialist Diploma in Applied Artificial Intelligence

About the Course

Course objectives

Upon completion of the Specialist Diploma in Applied Generative Artificial Intelligence (SDGAI) programme, graduates will be able to:

  • Explain the business cases of applied and generative artificial intelligence for different use cases or contexts.

  • List and describe the various technologies, frameworks, and platforms for applied and generative artificial intelligence.

  • Differentiate major Machine Learning, Deep Learning and Generative AI models.

  • Architect or design solutions that apply one or more artificial intelligence concept and technology using Machine Learning, Deep Learning or Generative AI models.

  • Build, test, and deploy a complete IT solution that applies artificial intelligence in industry use cases.

Course description

The Specialist Diploma in Applied Generative Artificial Intelligence (SDGAI) course is designed to provide a comprehensive introduction to the principles, techniques, and applications of Artificial Intelligence (AI) and Generative AI.

This course will guide participants through the fundamentals of AI, including machine learning, deep learning, and neural networks, before delving into the innovative realm of generative models such as GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), Stable Diffusion, and Large Language Models (LLMs). Participants will also gain a deep understanding of the ethical challenges in AI development and learn to develop AI systems with ethical considerations in mind.

By the end of this course, participants will have the knowledge and skills to implement AI solutions and create generative AI models that can produce original content.

What is so unique about this programme?

The course is designed to prepare students for industry certifications, specifically equiping them with the necessary skills needed to take the AWS Certified Machine Learning - Specialty exam and the Nvidia Deep Learning Institute certification.

Company Sponsorship

If you are a company-sponsored course applicant, please seek assistance from your company's course co-ordinator to log in to STEP using the Corp Pass to generate a corporate application link. Your application would need to be submitted via the link generated. For more information about using the STEP portal, please refer to our step-by-step guide.

Post-Diploma Certificate in Fundamentals of Artificial Intelligence

Programming for Artificial Intelligence

This module equips students with the fundamentals of programming using Python. Students will learn how to solve problems through coding a software program. Fundamentals on software structure, variables, selection, and iteration constructs will be covered. Students will be able to create software to solve simple programming problems related to AI.

Machine Learning Fundamentals

This module covers basic concepts in machine learning and the use of various open-source libraries like scikit-learn, Tensorflow and Keras to build basic machine learning application with perceptron, K nearest neighbours, neural networks, SVMs, decision trees and unsupervised learning. Students will also be introduced to XGBoost and other ensemble methods to solve classification and regression problems.

Deep Learning Fundamentals

This module aims to introduce participants to the key topics associated with deep learning. This module will cover the fundamental underpinnings of Artificial Neural Networks (ANN), Convolution Neural Networks (CNN), Recurrent Neural Networks (RNN) and Long-Short Term Memory (LSTM) networks and the fundamentals will be supported with practical work that involves participants developing and deploying an ANN, CNN, RNN, and LSTM.

Post-Diploma Certificate in Generative Artificial Intelligence

Developing Ethical AI Systems: Data, Values, and Process

This module offers an introduction to open-source data platforms and frameworks for storing large data volumes. It provides an overview of the machine learning pipeline including data management, high-throughput model training/evaluation, and real-time data processing. Participants will examine ethical/legal issues in AI using basic reasoning frameworks and principles, with a focus on privacy and data protection. Additionally, this module will cover AI project management methodologies like agile development, resource planning, and risk management tailored for AI projects. Concepts of explainable AI and model governance are introduced to ensure transparency, fairness, and compliance of AI systems.

Vision Models for Generative AI Applications

This module will journey through the world of vision models, making them work for tasks like understanding pictures and creating new, unique images. Starting with the basics, showing how these models learn from images. Then, the module will dive into how to tweak these models to make them even better, using tools from AWS, Nvidia, and the open-source community. Through hands-on experience, students will learn how to fine-tune these models for tasks like recognizing what is in a picture and even teaching the model to create its own images. The module is designed to be interactive and practical, giving students the skills needed to use these vision models effectively in the exciting world of generative AI.

Large Language Models for Generative AI Applications

This module delves into the technicalities of Large Language Models (LLMs), emphasizing their application in text-driven AI tasks. Starting with an insight into model training, the course intensifies its focus on fine-tuning these models for specific tasks such as text generation or summarization, leveraging technologies from AWS and Nvidia, alongside a variety of open-source resources. Students will gain a practical understanding of adjusting and deploying LLMs by diving into hands-on sessions that involve fine-tuning parameters and mastering deployment techniques. This approach ensures a comprehensive grasp of how to utilize these powerful models, employing a blend of industry-standard technologies and open-source tools to effectively implement LLMs in diverse AI applications.

Please click on the "Register" button to view the updated course schedule and fees on the Skills Training & Enhancement Portal (STEP).

At least a local Polytechnic Diploma or recognized Degree in science, engineering, information technology, or any other disciplines with at least three (3) years of relevant work experience.
Recognition of prior learning: Applicants who do not meet the entry requirements may be considered for admission to the course based on evidence of at least 5 years of relevant working experience or supporting evidence of competency readiness. Suitable applicants who are shortlisted may have to go through an interview and/or entrance test.

Conditional Offer

Graduating students (in final semester) from a local polytechnic may apply for the programme.
Click here for more information on how to apply for the programme and receive a Conditional Offer
The polytechnic reserves the right to shortlist and admit applicants.
Please click on the "Register" button to view the updated course schedule and fees on the Skills Training & Enhancement Portal (STEP).
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Last updated on 26 Feb 2025

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