Gifted Education Fund: Generative AI and AIoT (GenAIoT) Coding Skills Education for Gifted Students
Off-school Advanced Learning Programme (2023/24 school year):
Generative AI and AIoT (GenAIoT) Coding Skills Education for Gifted Students
(Funded by Gifted Education Fund)
Introduction
The Generative AI and AIoT (GenAIoT) Coding Skills Education for Gifted Students programme aims to equip gifted students with essential knowledge related to artificial intelligence (AI), generative AI (GenAI) and AI internet of things (AIoT) technologies, engineering and mathematics, problem solving abilities, as well as other hands-on skills including software and hardware programming.
The programme also aims to nurture positive values and attitudes among gifted students, such as ethics of using AI technology, ability to distinguish between real and fake contents, and perseverance to overcome problems, empowering them to become youth leaders with the right values.
Programme Objectives
- To provide gifted students with knowledge of foundation mathematics for use in AI and Python programming for development of relevant AI applications;
- To equip students with engineering skills and experiences, through lab sessions, assignments and projects, to develop IoT devices by using advanced hardware and software utilised in the industry;
- To develop gifted students’ hands-on coding abilities and problem solving skills to carry out AI projects, especially developing their own AI model, and have the ability to evaluate its quality;
- To enhance gifted students’ awareness of the applications and development of generative AI, LLM, GPT, etc, and encourage them to explore further to create their own applications;
- To inspire gifted students about the role and computation of System on Chip (SoC) and Integrated Circuit (IC) in AI;
- To arouse students’ awareness on ethics of using AI in daily life and its security issues, and nurture their ability to distinguish between real and generated fake contents; and
- To nurture positive values and attitudes among students and foster their perseverance in overcoming problems
Timeline
| Date and Time | Contents |
|---|---|
|
Phase 1: Foundational Guest Lectures on Generative AI and AIoT |
|
|
17 May 2024 (Fri) |
Foundational Guest Lecture on Generative AI and AIoT (I) |
|
24 May 2024 (Fri) |
Getting familiar with coding environments and GitHub |
|
31 May 2024 (Fri) |
Foundational Guest Lecture on Generative AI and AIoT (II) |
|
7 Jun 2024 (Fri) |
Python Packages for Machine Learning – NumPy |
|
14 Jun 2024 (Fri) |
Foundational Guest Lecture on Generative AI and AIoT (III) |
|
21 Jun 2024 (Fri) |
Python Packages for Machine Learning – NumPy (Cont’d) |
|
28 Jun 2024 (Fri) |
Foundational Guest Lecture on Generative AI and AIoT (IV) |
|
5 Jul 2024 (Fri) |
Python Packages for Machine Learning – Pandas, Scikit-Learn |
|
12 Jul 2024 (Fri) |
Python Packages for Machine Learning – Pandas, Numpy, OpenCV, Scikit-Learn (Cont’d) |
|
Phase 3: Basic Theory of AI and Machine Learning, Hands-on AI Coding in Python |
|
|
19 Jul 2024 (Fri) |
Definition, Developments and Applications of AI Supervised Learning in Detail |
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26 Jul 2024 (Fri) |
Different Kinds of Machine Learning Methods: Supervised Learning, Unsupervised Learning, Reinforcement Learning, Generative Adversarial Network (GAN) |
|
2 Aug 2024 (Fri) |
Supervised Learning in Detail: Regression vs Classification vs Learning to Rank, Loss Functions |
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9 Aug 2024 (Fri) |
Machine Learning Dataset Acquisition and Pre-processing (e.g. Fashion MNIST & CIFAR10 Datasets) |
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16 Aug 2024 (Fri) |
Overview of Deep Neural Network, its Training, and Inference |
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23 Aug 2024 (Fri) |
Lab Session: Image Recognition with Fashion MNIST Dataset |
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30 Aug 2024 (Fri) |
Convolutional Neural Network (CNN) |
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6 Sep 2024 (Fri) |
Overfitting, Regularizations and Object Detection |
|
Phase 4: AI Internet of Things (AIoT) Design with PYNQ |
|
|
13 Sep 2024 (Fri) |
Introduction to AI Internet of Things (AIoT) and Edge Computing |
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20 Sep 2024 (Fri) |
AI Processors, Ubuntu Linux Basics and Vim Editor |
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27 Sep 2024 (Fri) |
PYNQ - Python Productivity for Zynq and the Ultra96-V2 Board |
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4 Oct 2024 (Fri) |
Quantised Neural Network (QNN) and Model Compilation for the Inference on FPGA |
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18 Oct 2024 (Fri) |
Lab Session: Implementing the Deep Learning Processor on FPGA and Performing the Inference |
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25 Oct 2024 (Fri) |
Object Detection on FPGA with Real Time Streaming Protocol (RTSP) |
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Phase 5: Advances and Ethics of AI |
|
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1 Nov 2024 (Fri) |
AI Research Paper Study – How to Read a Research Paper, AI Open Source Platforms, such as Hugging Face |
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8 Nov 2024 (Fri) |
Introduction to Language Modeling |
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15 Nov 2024 (Fri) |
Attention Mechanism, Transformers, and Large Language Models (LLMs) such as Generative Pre-Trained Transformer (GPT) |
|
22 Nov 2024 (Fri) |
Text to Image Generation using LLM |
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29 Nov 2024 (Fri) |
Ethics of AI: AI Security and Explainable AI (XAI) |
|
Phase 6: Final Project Mentorship, Presentation, Competition and Exhibition |
|
|
6 Dec 2024 (Fri) |
Mentorship Meeting |
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13 Dec 2024 (Fri) |
Mentorship Meeting |
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Jan 2025 |
Student Project Competition, Exhibition and Closing Ceremony |
Notes:
• Class dates are subject to change.
• Classes will be conducted on campus unless otherwise necessary.
Medium of Instruction
Course Material: English
Class Teaching / Discussion: English supplemented with Cantonese
Application
Eligibility: Secondary 4-5* students in the 2023/24 school year who meet the following requirements:
- Demonstrate great interest and outstanding performance in mathematics, and should be experienced in at least one text-based computer programming language such as Python, C/C++, Swift, Java and JavaScript; and
- Show eagerness to learn AI, IoT, high performance computing, etc
* Junior form students with exceptional capabilities may also be considered on a case-by-case basis.
Programme Fee: Free of charge
Application Procedure: Please complete the application form (Chinese/ English) and send the scanned copy by email to eegefp@cityu.edu.hk, AND then the original copy by post on or before deadline to: Department of Electrical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong (Attn: Prof. Ray CHEUNG)
Application Deadline: 19 Apr 2024 (Fri)
Result Announcement Date : By early May 2024 (tentative)
More information can be found HERE.
Contact Information
Prof. Ray Cheung
Professor
Department of Electrical Engineering
City University of Hong Kong
Tel: 3442 9849
Email: eegefp@cityu.edu.hk