Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Spring Semester
Year 2026 360004 Artificial Intelligence 1 3.0 3 ★ Shu-Kai Fan GIEM
CMGT 19 0 工管所和管理博合開

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2026	360004	Artificial Intelligence	1	3.0	3	★	Shu-Kai Fan	GIEM CMGT	19	0	工管所和管理博合開

Course Plan：

Instructor Shu-Kai Fan　Office Hours

Email morrisfan@ntut.edu.tw

Last Updated 2025-12-25 16:48:02

Course Objective This course will focus on machine learning rather than hand-crafted knowledge engineering, due to the increased availability of data, computing resources, and new algorithms. Deep learning, probabilistic programming, and multi-agent systems receive expanded coverage, each with their own chapters. The coverage of natural language processing, robotics, and computer vision has been updated to reflect the impact of deep learning. The robotics chapter now includes robots that interact with humans and the application of reinforcement learning to robotics.

Course Schedule * Introduction * Deep Learning/Machine Learning * Loss Function and Entropy * Convolutional Neural Networks (CNNs) * 1-D CNNs * Visual Geometry Group (VGG) Net * Deep Residual Learning for Image Recognition * EfficientNet * Generative Adversarial Networks (GANs) * CycleGAN * Autoencoder (AE) * Convolutional Autoencoder (CAE) * Variational Autoencoder (VAE) * Denoising Autoencoder (DAE) * U-Net for image segmentation * Vision Transformer (ViT) * Diffusion models for image generation * Diffusion models for time series prediction * Reinforcement Learning: DDPG & PPO (if time allowed)

Evaluation and grading policy * Homework Assignments 80% * Participation 20% All homework assignments should be submitted individually. Students over the age of 40 may submit their assignments in pairs. 25% of Homework Assignments come from Qualcomm Edge-AI Group Term Project, including hands-on work plus the term-paper submission.

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
ISBN-13: 9781292401133 Artificial Intelligence: A Modern Approach 4th Stuart Russell (Author), Peter Norvig (Author) Pearson 2022 Additional lecturing materials in PDF will be provided.

The access to curricular consultation Tuesday 1400-1600

Expanding teaching and resources

The course corresponds to the SDGs SDG4：優質教育（Quality Education）
SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）

Does the course incorporate AI ● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process）
● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
● 使用生成式AI進行課堂即時互動（Use generative AI for real-time classroom interaction）
● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI）
● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）

Note
　

Instructor	Shu-Kai Fan　Office Hours
Email	morrisfan@ntut.edu.tw
Last Updated	2025-12-25 16:48:02
Course Objective	This course will focus on machine learning rather than hand-crafted knowledge engineering, due to the increased availability of data, computing resources, and new algorithms. Deep learning, probabilistic programming, and multi-agent systems receive expanded coverage, each with their own chapters. The coverage of natural language processing, robotics, and computer vision has been updated to reflect the impact of deep learning. The robotics chapter now includes robots that interact with humans and the application of reinforcement learning to robotics.
Course Schedule	* Introduction * Deep Learning/Machine Learning * Loss Function and Entropy * Convolutional Neural Networks (CNNs) * 1-D CNNs * Visual Geometry Group (VGG) Net * Deep Residual Learning for Image Recognition * EfficientNet * Generative Adversarial Networks (GANs) * CycleGAN * Autoencoder (AE) * Convolutional Autoencoder (CAE) * Variational Autoencoder (VAE) * Denoising Autoencoder (DAE) * U-Net for image segmentation * Vision Transformer (ViT) * Diffusion models for image generation * Diffusion models for time series prediction * Reinforcement Learning: DDPG & PPO (if time allowed)
Evaluation and grading policy	* Homework Assignments 80% * Participation 20% All homework assignments should be submitted individually. Students over the age of 40 may submit their assignments in pairs. 25% of Homework Assignments come from Qualcomm Edge-AI Group Term Project, including hands-on work plus the term-paper submission.
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes ISBN-13: 9781292401133 Artificial Intelligence: A Modern Approach 4th Stuart Russell (Author), Peter Norvig (Author) Pearson 2022 Additional lecturing materials in PDF will be provided.
The access to curricular consultation	Tuesday 1400-1600
Expanding teaching and resources
The course corresponds to the SDGs	SDG4：優質教育（Quality Education） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
Does the course incorporate AI	● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process） ● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools） ● 使用生成式AI進行課堂即時互動（Use generative AI for real-time classroom interaction） ● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI） ● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）
Note