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 355245 Machine Learning for Optoelectronic Systems 1 3.0 3 ★ Yibeltal Chanie Manie 4EOE2 52 0 EMI課程

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2026	355245	Machine Learning for Optoelectronic Systems	1	3.0	3	★	Yibeltal Chanie Manie	4EOE2	52	0	EMI課程

Course Plan：

Instructor Yibeltal Chanie Manie　Office Hours

Email yibeltal_chanie@mail.ntut.edu.tw

Last Updated 2025-12-17 12:26:20

Course Objective 1. 機器學習簡介及基本概念 2. 用於分類、迴歸和分群的機器學習演算法 3. 機器學習模型訓練、評估與最佳化技術 4. 機器學習在光電系統的應用 1. Introduction and basic concepts of machine learning 2. Machine learning algorithms for classification, regression and clustering 3. Machine learning model training, evaluation, and optimization technology 4. Application of machine learning in optoelectronic systems

Course Schedule Week 1: Course overview Week 2. Introduction and basic concepts of machine learning Week 3. Types of Machine learning Week 4-9. Machine learning algorithms for classification, regression and clustering Week 10-13. Machine learning model training, evaluation, and optimization technology Wek 14-17. Application of machine learning in optoelectronic systems Week 18. Final Project presentation

Evaluation and grading policy HomeWorks: 40% Lab Quiz's : 20% Class Participation and Attendance: 10% Final project and report: 30%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
References: 1. Ethem Alpaydin, Introduction to Machine Learning, PHI 3rd edition, 2014 2. Peter Flach, Machine Learning: the art and science of algorithms that make sense of data, Cambridge university press, edition 2012.

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

The course corresponds to the SDGs 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）

Note
　

Instructor	Yibeltal Chanie Manie　Office Hours
Email	yibeltal_chanie@mail.ntut.edu.tw
Last Updated	2025-12-17 12:26:20
Course Objective	1. 機器學習簡介及基本概念 2. 用於分類、迴歸和分群的機器學習演算法 3. 機器學習模型訓練、評估與最佳化技術 4. 機器學習在光電系統的應用 1. Introduction and basic concepts of machine learning 2. Machine learning algorithms for classification, regression and clustering 3. Machine learning model training, evaluation, and optimization technology 4. Application of machine learning in optoelectronic systems
Course Schedule	Week 1: Course overview Week 2. Introduction and basic concepts of machine learning Week 3. Types of Machine learning Week 4-9. Machine learning algorithms for classification, regression and clustering Week 10-13. Machine learning model training, evaluation, and optimization technology Wek 14-17. Application of machine learning in optoelectronic systems Week 18. Final Project presentation
Evaluation and grading policy	HomeWorks: 40% Lab Quiz's : 20% Class Participation and Attendance: 10% Final project and report: 30%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes References: 1. Ethem Alpaydin, Introduction to Machine Learning, PHI 3rd edition, 2014 2. Peter Flach, Machine Learning: the art and science of algorithms that make sense of data, Cambridge university press, edition 2012.
The access to curricular consultation	email: yibeltal_chanie@mail.ntut.edu.tw
The course corresponds to the SDGs	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）
Note