查詢教學大綱與進度

教學大綱與進度

課程基本資料：

學年期課號課程名稱階段學分時數修教師班級人撤備註

114-2 353959 人工智慧開發與安全 1 3.0 3 ★ 彭祖乙資財四甲 9 0 資財四甲、資財所合開

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
114-2	353959	人工智慧開發與安全	1	3.0	3	★	彭祖乙	資財四甲	9	0	資財四甲、資財所合開

教學大綱與進度：

教師姓名彭祖乙　教師諮商時間(Office Hours)

Email johnpeng@mail.ntut.edu.tw

最後更新時間 2025-12-19 16:09:19

課程大綱本課程旨在介紹人工智慧（AI）領域包含各種機器學習的開發與實作，特別關注如何使用Python進行模型的開發與部屬。課程將涵蓋深度學習的基本概念、常見的AI框架（如TensorFlow、Keras），並深入探討如何利用Python編程語言來設計、訓練與優化AI模型。此外，課程也將重點討論人工智慧在開發與部屬過程中需要考量的安全性問題，從資料安全與可稽核性，AI透明度，模型加密與攻擊防範等，提供學生一套全面的安全檢核框架。學生將學習如何確保AI系統在實際應用中具備高度的安全性與可靠性，並瞭解如何在AI部屬的不同階段識別與解決潛在的安全風險。 This course aims to introduce the development and implementation of various machine learning techniques in the field of Artificial Intelligence (AI), with a particular focus on how to use Python for model development and deployment. The course will cover the fundamental concepts of deep learning, popular AI frameworks (such as TensorFlow and Keras), and will delve into how to use the Python programming language to design, train, and optimize AI models. Additionally, the course will emphasize the security considerations necessary during the development and deployment of AI systems, including data security and auditability, AI transparency, model encryption, and attack prevention. Students will be provided with a comprehensive security framework to evaluate these concerns. They will also learn how to ensure the high security and reliability of AI systems in practical applications, and how to identify and mitigate potential security risks at various stages of AI deployment.

課程進度 Week 1: Introduction Week 2: Python review Week 3: ML data processing Week 4: Supervised learning algorithm Week 5: Unsupervised learning algorithm Week 6: NN Basic and PBL project Week 7: Forward propagation and Back propagation Week 8: CNN and Dataset Week 9: Mid-term Week 10:　RNN & LSTM Week 11-13: Generative AI Week 14: Reinforcement Learning Week 15,16 AI DevOPS Security Week 17 & 18 Final Term

評量方式與標準 • Homework, class participation: 30% • Midterm report & presentation: 30% • Final project: 40%

使用教材、參考書目或其他 【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】

使用外文原文書：是
Google ML Course : https://developers.google.com/machine-learning Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow (3nd Ed.)

課程諮詢管道 johnpeng@mail.ntut.edu.tw

The course website will be listed on
https://tipeng.wixsite.com/school/

延伸教學與資源

課程對應SDGs指標 SDG4：優質教育（Quality Education）
SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）

課程是否導入AI ● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）

備註

教師姓名	彭祖乙　教師諮商時間(Office Hours)
Email	johnpeng@mail.ntut.edu.tw
最後更新時間	2025-12-19 16:09:19
課程大綱	本課程旨在介紹人工智慧（AI）領域包含各種機器學習的開發與實作，特別關注如何使用Python進行模型的開發與部屬。課程將涵蓋深度學習的基本概念、常見的AI框架（如TensorFlow、Keras），並深入探討如何利用Python編程語言來設計、訓練與優化AI模型。此外，課程也將重點討論人工智慧在開發與部屬過程中需要考量的安全性問題，從資料安全與可稽核性，AI透明度，模型加密與攻擊防範等，提供學生一套全面的安全檢核框架。學生將學習如何確保AI系統在實際應用中具備高度的安全性與可靠性，並瞭解如何在AI部屬的不同階段識別與解決潛在的安全風險。 This course aims to introduce the development and implementation of various machine learning techniques in the field of Artificial Intelligence (AI), with a particular focus on how to use Python for model development and deployment. The course will cover the fundamental concepts of deep learning, popular AI frameworks (such as TensorFlow and Keras), and will delve into how to use the Python programming language to design, train, and optimize AI models. Additionally, the course will emphasize the security considerations necessary during the development and deployment of AI systems, including data security and auditability, AI transparency, model encryption, and attack prevention. Students will be provided with a comprehensive security framework to evaluate these concerns. They will also learn how to ensure the high security and reliability of AI systems in practical applications, and how to identify and mitigate potential security risks at various stages of AI deployment.
課程進度	Week 1: Introduction Week 2: Python review Week 3: ML data processing Week 4: Supervised learning algorithm Week 5: Unsupervised learning algorithm Week 6: NN Basic and PBL project Week 7: Forward propagation and Back propagation Week 8: CNN and Dataset Week 9: Mid-term Week 10:　RNN & LSTM Week 11-13: Generative AI Week 14: Reinforcement Learning Week 15,16 AI DevOPS Security Week 17 & 18 Final Term
評量方式與標準	• Homework, class participation: 30% • Midterm report & presentation: 30% • Final project: 40%
使用教材、參考書目或其他	【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】使用外文原文書：是 Google ML Course : https://developers.google.com/machine-learning Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow (3nd Ed.)
課程諮詢管道	johnpeng@mail.ntut.edu.tw The course website will be listed on https://tipeng.wixsite.com/school/
延伸教學與資源
課程對應SDGs指標	SDG4：優質教育（Quality Education） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
課程是否導入AI	● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）
備註