基本素養 Basic Literacy

人文與倫理素養
Quality of humanism and ethics.
誠信素養
Integrity.
社會關懷
Care for society.

核心能力 Competence

工程領域之專業知識
Professional knowledge in engineering field.
策劃及執行專題研究之能力
Plan and execute case study.
撰寫專業論文之能力
Write professional essays.
創新思考及獨立解決問題之能力
Creative thinking and independent problem-solving ability.
與不同領域人員協調整合之能力
Integration and negotiation with other fields’ experts.
廣闊之國際視野
Wide global vision.
領導、管理及規劃的能力
Leadership, management, arrangement ability.
終身自我學習成長之能力
Lifelong self-learning ability.
國際視野
Global vision.

課程概述 Course Description

人工神經網絡是在1950年代引入的,然而,在過去十年中,神經網絡處理技術已經應用於許多解決現實問題。這讓圖像,視頻和自然語言處理等有許多突破性的應用。本課程旨在介紹甚麼是類神經網路、其工作原理與架構為何、它能做甚麼應用、如何將神經網路應用到工程與人工智慧場域。討論一些有名的神經網路模式,提供學生足夠的相關細節,以讓它們能實踐類神經網路的應用,並透過MATLAB軟體及一些免費的自由軟體,如Caffe和Torch建立自行的神經網路系統來完成專題實驗。
Artificial neural networks were introduced in the 1950s. However, in the past decade, neural network processing techniques have been applied to many real-world problems. This allows for many groundbreaking applications such as image, video and natural language processing. This course aims to introduce what a neural network is, how it works and its architecture, what applications it can make, and how to apply neural networks to the engineering and artificial intelligence fields. Discuss some well-known neural network models, provide students with enough relevant details to enable them to practice neural network applications, and build their own neural network systems through MATLAB software and some free free software such as Caffe and Torch To complete a thematic experiment.

課程學習目標 Course Objectives

  • 1.學習如何使用和開發現代軟體工具,將神經網路應用於現實世界的問題
  • 2.進行比較分析,包括理論和經驗,以確定哪個神經網絡最適合解特定之問題
  • 3對沒有深入此項技術知識的人解釋基於神經網路的解決方案的優點和局限性
  • 4.自己可以有能力構建不同類型的神經網絡,評估其性能,並使用它們來解決複雜問題。
  • 5.閱讀該領域的文章,了解它並進行批判性分析
  • 課程進度 Progress Description

    進度說明 Progress Description
    1神經網路簡介
    2線性代數,機率學等相關基礎數學介紹
    3類神經網路基本組成架構與應用例子說明
    4單層和多層前饋神經網路結構與應用
    5倒傳遞(BP)類神經網路架構與訓練
    6倒傳遞(BP)類神經網路之應用
    7支持向量(SVM)機與徑向基礎(Radial Basis)神經網路(一)
    8支持向量機(SVM)與徑向基礎(Radial Basis)神經網路(二)與應用實現
    9期中考
    10自組織對映(Self-organizing map, SOM)神經網路架構
    11自組織對映(SOM)神經網路非監督式學習應用
    12自適應共振理論(ART)
    13捲積類神經網路(CNN)及應用(一)
    14捲積類神經網路(CNN)及應用(二)
    15遞歸神經網路(RNN)和長短期記憶模型(LSTM)的運作原理與應用
    16專題製作
    17專題製作
    18學期期末考
     以上每週進度教師可依上課情況做適度調整。The schedule may be subject to change.

    有關課程其他調查 Other Surveys of Courses

    1.本課程是否規劃業界教師參與教學或演講?
    Is there any industry specialist invited in this course? How many times?
    2.本課程是否規劃含校外實習(並非參訪)?
    Are there any internships involved in the course? How many hours?
    3.本課程是否可歸認為學術倫理課程? 否
    Is this course recognized as an academic ethics course? In the course how many hours are regarding academic ethics topics? No
    4.本課程是否屬進入社區實踐課程?
    Is this course recognized as a Community engagement and Service learning course? Which community will be engaged?