基本素養 Basic Literacy

倫理推論
學生應具備良好倫理道德觀
Ethical Reasoning
Graduate students should be able to identify ethical dilemmas and to determine necessary courses of action.
全球化思維
學生需具掌握國際間資料科學分析方法的更替、及全球商務變化趨勢的能力
Global Vision
Graduate students should possess a global data science perspective and an awareness of the global business.

核心能力 Competence

口頭表達
學生應具備資料分析概念之溝通表達能力
Speaking
Graduate students should be able to appreciate data analysis approaches and to present research findings/ results effectively in speaking and in writing.
寫作能力
學生應具備良好邏輯之寫作能力
Writing
Graduate students should be able to appreciate data analysis approaches and to present research findings/ results effectively in speaking and in writing.
跨領域性之融合與解題
學生應能針對各不同領域面臨的資料分析問題並提出解決方法
Interdiscip. Competence/ Prob. Solving
Graduate students should be able to integrate different functional areas in solving data analysis problems.
批判思考及創新力
學生應具備良好的批判、思考及創新之能力
Critical Thinking/ Innovation
Graduate students should be able to analyze data effectively and to recommend effective statistical methods.
領導能力
學生應具備良好領導能力
Leadership
Graduate students should be able to demonstrate leadership skills as a data analysis team leader.
團隊合作
學生能在群體中具溝通、領導能力,並運用統計方法與其他背景專長者,一同解決問題
Teamwork
Graduate students should be able to coordinate actions and solve problems jointly with other members of a professional team.

課程概述 Course Description

Artificial intelligence (deep learning) has achieved great success in many fields. For example, image recognition, medical image analysis, and self-driving cars. Each application has a specific neural network model and a learning strategy in the field of deep learning. This course will explore the deep learning architecture details and use computer vision-related applications as examples to introduce learning strategies. This course contains not only these essential expositions of deep Learning but also many practical programming tasks. The goal is to give students the ability to build a corresponding deep neural network based on their problems.
Artificial intelligence (deep learning) has achieved great success in many fields. For example, image recognition, medical image analysis, and self-driving cars. Each application has a specific neural network model and a learning strategy in the field of deep learning. This course will explore the deep learning architecture details and use computer vision-related applications as examples to introduce learning strategies. This course contains not only these essential expositions of deep Learning but also many practical programming tasks. The goal is to give students the ability to build a corresponding deep neural network based on their problems.

課程學習目標 Course Objectives

  • Introduce deep neural networks architecture and its application.
  • The student is the ability to choose a network approach for their studies.
  • The student is capable of having factual programming skills for deep learning.
  • 課程進度 Progress Description

    進度說明 Progress Description
    1Course Introduction
    2Machine learning: The data-driven approach
    3Loss Functions and Optimization
    4Introduction to Neural Networks / Multilayer Perceptron
    5Convolutional Neural Networks
    6Intro to Pytorch and Tensorflow under Ubuntu System
    7CNN Architectures: AlexNet, VGG / Final Project Proposal due
    8Training Neural Networks
    9In-class midterm
    10CNN Architectures: GoogLeNet, ResNet, DenseNet, EfficientNet, etc
    11Proposal presentation
    12Object Detection
    13Semantic Segmentation
    14Unsupervised Learning: Generative Models
    15Recurrent Neural Networks
    16Semi-Supervised learning
    17Invited talk
    18Final Project Presentation
     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.

    課程是否與永續發展目標相關調查
    Survey of the conntent relevant to SDGs

    本課程與SDGs相關項目如下:
    This course is relevant to these items of SDGs as following:
    • 就業與經濟成長 (Decent work and Economic growth)

    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? 是,約 1 次
    Is there any industry specialist invited in this course? How many times? Yes, about 1 times.
    2.本課程是否規劃含校外實習(並非參訪)? 否
    Are there any internships involved in the course? How many hours? No
    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? 否
    Is this course recognized as a Community engagement and Service learning course? Which community will be engaged? No