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 345984 Machine Learning Practice 1 3.0 3 ★ TSUI PENG 4IFM2A 35 0 　

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2025	345984	Machine Learning Practice	1	3.0	3	★	TSUI PENG	4IFM2A	35	0

Course Plan：

Instructor TSUI PENG　Office Hours

Email johnpeng@mail.ntut.edu.tw

Last Updated 2024-12-20 14:55:44

Course Objective 本課程將帶領學生從基礎的機器學習概念開始，逐步學習各種機器學習演算法與實作技巧，並將所學應用於實際問題中。課程內容涵蓋監督式學習、非監督式學習、強化學習等機器學習範疇，並將使用 Python 程式語言與 scikit-learn、TensorFlow 等熱門機器學習框架進行實作。本課程預計使用 EMI 授課。 This course will guide students from fundamental machine learning concepts, gradually learning various machine learning algorithms and implementation techniques, and applying what they have learned to real-world problems. The course content covers supervised learning, unsupervised learning, reinforcement learning, and other areas of machine learning. Students will use Python programming language along with popular machine learning frameworks such as scikit-learn and TensorFlow for hands-on implementation.

Course Schedule Week 1: Introduction Week 2: Python review Week 3: ML tools Week 4: ML data processing Week 5: Regression Week 6: Supervised learning algorithm (1) Week 7: Supervised learning algorithm (2) Week 8: Unsupervised learning algorithm (2) Week 9: Mid-term Week 10: NN Basic and PBL project Week 11: Forward propagation Week 12: Back Propagation Week 13: CNN(1) Week 14: CNN(2) and Dataset Week 15:　RNN & LSTM Week 16: ML Deployment Week 17 & 18 Final Term

Evaluation and grading policy • Homework, class participation: 30% • Midterm report & presentation: 30% • Final project: 40%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
Google ML Course : https://developers.google.com/machine-learning Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow (3nd Ed.)

The access to curricular consultation johnpeng@mail.ntut.edu.tw

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

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）

Note
　

Instructor	TSUI PENG　Office Hours
Email	johnpeng@mail.ntut.edu.tw
Last Updated	2024-12-20 14:55:44
Course Objective	本課程將帶領學生從基礎的機器學習概念開始，逐步學習各種機器學習演算法與實作技巧，並將所學應用於實際問題中。課程內容涵蓋監督式學習、非監督式學習、強化學習等機器學習範疇，並將使用 Python 程式語言與 scikit-learn、TensorFlow 等熱門機器學習框架進行實作。本課程預計使用 EMI 授課。 This course will guide students from fundamental machine learning concepts, gradually learning various machine learning algorithms and implementation techniques, and applying what they have learned to real-world problems. The course content covers supervised learning, unsupervised learning, reinforcement learning, and other areas of machine learning. Students will use Python programming language along with popular machine learning frameworks such as scikit-learn and TensorFlow for hands-on implementation.
Course Schedule	Week 1: Introduction Week 2: Python review Week 3: ML tools Week 4: ML data processing Week 5: Regression Week 6: Supervised learning algorithm (1) Week 7: Supervised learning algorithm (2) Week 8: Unsupervised learning algorithm (2) Week 9: Mid-term Week 10: NN Basic and PBL project Week 11: Forward propagation Week 12: Back Propagation Week 13: CNN(1) Week 14: CNN(2) and Dataset Week 15:　RNN & LSTM Week 16: ML Deployment Week 17 & 18 Final Term
Evaluation and grading policy	• Homework, class participation: 30% • Midterm report & presentation: 30% • Final project: 40%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Google ML Course : https://developers.google.com/machine-learning Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow (3nd Ed.)
The access to curricular consultation	johnpeng@mail.ntut.edu.tw The course website will be listed on https://tipeng.wixsite.com/school/
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）
Note