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

機器學習是一門新近的熱門研究領域,主要探討如何在高維度、大量或複雜資料中,使用統計方法來發展不同的演算法來發掘資料中所隱藏的有用資訊。
Machine Learning is a popular research area lately. The main goal is to investigate the high dimensional, large amount or complex data, and use statistical methods to develop the useful algorithm to discover information within data.

課程學習目標 Course Objectives

  • 了解各種機器學習方法之原理
  • 熟悉各種機器學習手刻演算法與套件應用
  • 認識最新機器學習技術
  • 課程進度 Progress Description

    進度說明 Progress Description
    1Course Introduction
    2Tree-based Models 1: Decision Tree
    3Tree-based Models 2: Bagging and Boosting
    4Logistic Regression and Optimization
    5Support Vector Machine
    6MLP and Neural Networks
    7From NN to Deep Learning
    8Convolutional Neural Network
    9Recurrent Neural Network
    10Embedding Learning 1
    11Embedding Learning 2
    12Attention Mechanisms
    13Autoencoder
    14Final Project Proposal
    15Self-Supervised Learning
    16Generative Adversarial Network
    17Final Project Presentation 1
    18Final Project Presentation 2
     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:
    • 健康與福祉 (Good health and Well Being)
    • 就業與經濟成長 (Decent work and Economic growth)
    • 工業、創新與基礎建設 (Industry Innovation and infrastructure)
    • 和平與正義制度 (Peace justice and strong institutions)

    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

    教師上傳大綱內容
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