資訊工程學系(Department of Computer Science and Information Engineering)
課程名稱(英文)
Introduction of Machine Learning
課程代碼
4103062_01
授課教師：
盧沛怡
學分數
3
必/選修
選修
開課年級
大三
先修科目或先備能力：
Computer programming, probability, linear algebra
課程概述：
This course provides a comprehensive overview of essential machine learning principles and techniques which covers supervised and unsupervised learning, Bayesian methods, and model selection. Students will study algorithms such as linear regression, logistic regression, neural networks, and kernel methods, with a focus on practical applications and hands-on exercises.
學習目標：
1. Understand the fundamental concepts and techniques in machine learning.
2. Distinguish supervised and unsupervised learning with applications.
3. Apply Bayesian methods and model selection.
4. Implement algorithms such as regression, neural networks, and kernel methods.
5. Evaluate and improve model performance with appropriate metrics.
1.1.具有資訊工程相關基礎知識之吸收與了解的能力(Capability to grasp foundational knowledge in computer science.)
1.2.具有運用資訊工程理論及應用知識，分析與解決相關問題的能力(Capability to use computer science theory and application knowledge to analyze and solve related problems.)
1.3.在資訊工程的許多領域中，具有至少某一項專業能力，例如：硬體、軟體、多媒體、系統、網路、理論等(Professional in at least one area, including hardware, software, multimedia, system, networking, and theory.)
2.1.具有資訊工程實作技術及使用計算機輔助工具的能力(Capability to perform computer science implementations and use computer-aided tools.)
2.2.具有設計資訊系統、元件或製程的能力(Capability to design computer systems, components, or processes.)
2.3.具有科技寫作與簡報的能力。(Capability to write and present technical materials.)
3.1.具有除了已有的應用領域之外，亦可以將自己的專業知識應用於新的領域或跨多重領域，進行研發或創新的能力。(Capability to apply one’s professional knowledge to a new application domain or across multiple different application domains.)
3.2.具有領導或參與一個團隊完成一項專案任務的能力並且具有溝通、協調與團隊合作的能力。(Capability to lead or participate in group projects, with effective communication, coordination, and teamwork.)
3.3.具有因應資訊科技快速變遷之能力，培養自我持續學習之能力。(Capability to adapt to rapidly changing computer science technology and to develop self-learning capabilities.)
4.1.具有社會責任、人文素養及奉獻精神。(The awareness of social responsibilities, humanity, and contribution.)
4.2.具有工程倫理、宏觀能力、國際觀及前瞻視野。(The awareness of engineering ethics, broad capabilities, and global and contemporary vision.)
具有資訊工程相關基礎知識之吸收與了解的能力(Capability to grasp foundational knowledge in computer science.)
為何有關：
Machine learning is a crucial technique in data analytics. After studying machine learning, students will gain a deeper understanding of fundamental computer science subjects, such as probability, linear algebra, and programming.
達成指標：
Students should gain a comprehensive understanding of the prevalent techniques in machine learning.
評量工具(可複選)：
Assignments, exam, final project
1.2
具有運用資訊工程理論及應用知識，分析與解決相關問題的能力(Capability to use computer science theory and application knowledge to analyze and solve related problems.)
為何有關：
After completing this course, students will be able to apply the skills learned to solve real-world problems.
達成指標：
Address real-world problems using machine learning algorithms.
評量工具(可複選)：
Assignments, final project
1.3
在資訊工程的許多領域中，具有至少某一項專業能力，例如：硬體、軟體、多媒體、系統、網路、理論等(Professional in at least one area, including hardware, software, multimedia, system, networking, and theory.)
為何有關：
This machine learning course is connected to various areas of computer science, such as software development, system design, and theoretical analysis. Students will apply machine learning techniques to solve complex problems in these fields, thereby enhancing their professional capabilities in specific domains.
達成指標：
Ability to apply machine learning techniques in various professional areas of computer science, including software, systems, and theory.
評量工具(可複選)：
Assignments, final project
2.1
具有資訊工程實作技術及使用計算機輔助工具的能力(Capability to perform computer science implementations and use computer-aided tools.)
為何有關：
This course introduces existing tools to implement and test machine learning algorithms and models.
達成指標：
Ability to use computer-aided tools to design, implement, and test machine learning models.
評量工具(可複選)：
Assignments, final project
2.3
具有科技寫作與簡報的能力。(Capability to write and present technical materials.)
為何有關：
Students are required to independently present the final project.
達成指標：
To deliver a presentation of their final project.
評量工具(可複選)：
final project
3.1
具有除了已有的應用領域之外，亦可以將自己的專業知識應用於新的領域或跨多重領域，進行研發或創新的能力。(Capability to apply one’s professional knowledge to a new application domain or across multiple different application domains.)
為何有關：
Students are required to apply machine learning methods on real-world applications for their final project.
達成指標：
To accomplish the final project.
評量工具(可複選)：
assignments, final project
3.3
具有因應資訊科技快速變遷之能力，培養自我持續學習之能力。(Capability to adapt to rapidly changing computer science technology and to develop self-learning capabilities.)
為何有關：
As AI emerges and evolves, machine learning technologies are rapidly advancing. Students need to learn the latest techniques and methods and utilize tools to implement and test machine learning algorithms and models. This course covers these aspects, enabling students to adapt to these fast-paced developments.
達成指標：
Ability to understand and apply the latest technologies and methods in machine learning.