Course Syllabus

Course Information：

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

Fall Semester
Year 2026 366989 Machine Learning 1 3.0 3 ★ WEI-HO TSAI AI雙聯專班 1 0 碩一修

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2026	366989	Machine Learning	1	3.0	3	★	WEI-HO TSAI	AI雙聯專班	1	0	碩一修

Course Plan：

Instructor WEI-HO TSAI　Office Hours

Email whtsai@ntut.edu.tw

Last Updated 2026-06-29 13:43:12

Course Objective This course introduces the concepts, techniques, and applications of machine learning. Topics mainly include Symbolists (e.g., Decision Tree), Connectionists (e.g., Artificial Neural Networks), Bayesians (e.g., Hidden Markov Model), Analogizers (e.g., kNN), and Evolutionaries (e.g., GA), Natural Language Processing and Speech Recognition. The lecture format will be a combination of slide presentation, chalk talk and computer demonstration. In addition to the theoretical study, students are guided to survey the related references, discuss and brainstorm by group, and conduct the practice projects, so that the capability of creativity and innovation can be developed during the course.

Course Schedule 1. Introduction to Machine Learning (1hr) 2. Supervised Learning, e.g., Decision Tree, NB, kNN, SVM, Hidden Markov Model, and Artificial Neural Networks (9hr) 3. Unsupervised Learning, e.g., K-means Clustering, Hierarchical Clustering (9hr) 4. Machine Learning with WEKA (9hr) 5. Evolutionary Computing (8hr) 6. Natural Language Processing (5hr) 7. Speech Recognition (9hr) 8. Students' Project Presentation (4hr)

Evaluation and grading policy Attendance and participation 30% Assignments 30% Oral presentation 40%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
1. "Data Mining - Practical Machine Learning Tools and Techniques (2nd Edition)" by Ian Witten and Eibe Frank, eBook ISBN: 9780080477022 (available in our school's library) 2. Terje Kristensen, Computational Intelligence, Evolutionary Computing and Evolutionary Clustering Algorithms. Sharjah, U.A.E.: Bentham Science Publishers. 2016 3. Dong Yu and Li Deng, Automatic Speech Recognition, A Deep Learning Approach. Springer, 2015

The access to curricular consultation email: whtsai@ntut.edu.tw

Expanding teaching and resources ● 混成學習 (Blended Learning)
課程結合實體授課與線上學習（同步視訊／非同步活動）。
Combines face-to-face instruction with online learning (including synchronous video sessions or asynchronous activities).

The course corresponds to the SDGs SDG3：良好健康和福祉（Good Health and Well-being）

Does the course incorporate AI ● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
● 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
上課時同步使用MS Teams視訊，詳細資訊將公告於北科i學園，或請洽 whtsai@ntut.edu.tw 詢問。

Instructor	WEI-HO TSAI　Office Hours
Email	whtsai@ntut.edu.tw
Last Updated	2026-06-29 13:43:12
Course Objective	This course introduces the concepts, techniques, and applications of machine learning. Topics mainly include Symbolists (e.g., Decision Tree), Connectionists (e.g., Artificial Neural Networks), Bayesians (e.g., Hidden Markov Model), Analogizers (e.g., kNN), and Evolutionaries (e.g., GA), Natural Language Processing and Speech Recognition. The lecture format will be a combination of slide presentation, chalk talk and computer demonstration. In addition to the theoretical study, students are guided to survey the related references, discuss and brainstorm by group, and conduct the practice projects, so that the capability of creativity and innovation can be developed during the course.
Course Schedule	1. Introduction to Machine Learning (1hr) 2. Supervised Learning, e.g., Decision Tree, NB, kNN, SVM, Hidden Markov Model, and Artificial Neural Networks (9hr) 3. Unsupervised Learning, e.g., K-means Clustering, Hierarchical Clustering (9hr) 4. Machine Learning with WEKA (9hr) 5. Evolutionary Computing (8hr) 6. Natural Language Processing (5hr) 7. Speech Recognition (9hr) 8. Students' Project Presentation (4hr)
Evaluation and grading policy	Attendance and participation 30% Assignments 30% Oral presentation 40%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes 1. "Data Mining - Practical Machine Learning Tools and Techniques (2nd Edition)" by Ian Witten and Eibe Frank, eBook ISBN: 9780080477022 (available in our school's library) 2. Terje Kristensen, Computational Intelligence, Evolutionary Computing and Evolutionary Clustering Algorithms. Sharjah, U.A.E.: Bentham Science Publishers. 2016 3. Dong Yu and Li Deng, Automatic Speech Recognition, A Deep Learning Approach. Springer, 2015
The access to curricular consultation	email: whtsai@ntut.edu.tw
Expanding teaching and resources	● 混成學習 (Blended Learning) 課程結合實體授課與線上學習（同步視訊／非同步活動）。 Combines face-to-face instruction with online learning (including synchronous video sessions or asynchronous activities).
The course corresponds to the SDGs	SDG3：良好健康和福祉（Good Health and Well-being）
Does the course incorporate AI	● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools） ● 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	上課時同步使用MS Teams視訊，詳細資訊將公告於北科i學園，或請洽 whtsai@ntut.edu.tw 詢問。