Course Syllabus

Course Information：

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2025	345313	Generative AI technology and applications	1	3.0	3	★	Tz-heng, Fu	4IxD3	35	0	外生三與大三合開,限本系生35人

Course Plan：

Instructor	Tz-heng, Fu　Office Hours
Email	jomo0825@gmail.com
Last Updated	2025-01-10 01:10:48
Course Objective	The breakthrough in Generative AI (GAI) technology has brought about a significant transformation in multimedia content design, with AI-generated videos, images, and text approaching the proficiency level of professionals. This course focuses on two major technologies: Large Language Models (LLM) and Diffusion Models, utilizing open-source tools such as Llama and Stable Diffusion for hands-on practice. In addition to providing an in-depth and comprehensive explanation of the working principles of LLMs and Diffusion Models, the course also teaches students the fundamental operation of AI and how to efficiently produce high-quality and creative AI-generated content. By mastering generative AI technology and its applications, students will be able to embed AI-generated multimedia content into various information systems, significantly enhancing system functionality, aesthetics, and user experience. This course aims to equip students with solid skills in learning, research, and application, achieving a human-AI co-creation of hypermedia content. * Both theories and applications will be covered. * Use Python + Keras for coding in W01~W06.
Course Schedule	02/20 W1 Overview of Generative AI 02/27 W2 Introduction to Diffusion Models 03/06 W3 Clip Prompt Engineering 03/13 W4 Fine-tuning a Diffusion Model - TI 03/20 W5 Fine-tuning a Diffusion Model - DB 03/27 W6 ControlNet 04/03 W7 Introduction to LLMs – Prompt engineering 04/10 W8 LLM applications - Reading/Writing/Coding 04/17 W9 Mid-term Week 04/24 W10 LLM Fine-tuning 05/01 W11 LLM RAG 05/08 W12 LLM AI Agent 05/15 W13 Basics of Generative AI (1) – Linear algebra, Back propagation, Linear regression, Classifier 05/22 W14 Basics of Generative AI (2) – Auto encoder, VAE 05/29 W15 Basics of Generative AI (3) – RNN, Transformer 06/05 W16 Basics of Generative AI (4) – Diffusion model 06/12 W17 PBL 06/19 W18 Final Week
Evaluation and grading policy	* Participation: 10% * Mid-term: 40% * Final Report: 50%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes https://www.amazon.com/Generative-Deep-Learning-Teaching-Machines-dp-1098134184/dp/1098134184/ref=dp_ob_image_bk
The access to curricular consultation	Will launch an MS Teams group when the class starts.
The course corresponds to the SDGs	SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
Note	* You'll need to run AI on GPU. For running on a local Windows system, a 8GB VRAM graphic card is required. RTX 2070 8GB * If you don't have a qualified GPU, you may consider to purchase Runpod or Google Colab. For each week roughly USD$10 expense is estimated. Actual expenses fluctuate according to personal exercise usage.