國立中山大學108學年度第1學期深度學習課程大綱

國立中山大學 108學年度第1學期課程教學大綱

National Sun Yat-sen University 108Academic year1st Semester Course syllabus

中文名稱
Course name(Chinese)

深度學習

課號
Course Code

MIS583

英文名稱
Course name(English)

DEEP LEARNING

課程類別
Type of the course

講授類

必選修
Required/Selected

選修

系所
Dept./faculty

資訊管理學系碩士班

授課教師
Instructor

楊惠芳

學分
Credit

因應嚴重特殊傳染性肺炎(武漢肺炎)，倘若後續需實施遠距授課，授課方式調整如下：

尚未建立傳染性肺炎(武漢肺炎)授課方式調整

因應嚴重特殊傳染性肺炎(武漢肺炎)，倘若後續需實施遠距授課，評分方式調整如下：

尚未建立傳染性肺炎(武漢肺炎)課程評分方式﹝評分標準及比例﹞

課程大綱 Course syllabus

本課程教學大綱已提供完整英文資訊（本選項僅供統計使用，未提供完整英文資訊者，得免勾記）【Provide information of course syllabus in English.(This is for statistical use only. For those who do not provide information of course syllabus in English, do not check this field.)】

本課程是一門深度學習入門課程，強調理論與實務並重，介紹深度學習的理論及概念並培養實作能力。課程編排包括一系列關於各種深度學習模型的技術課程，並輔以一系列實作，以期培養學生整合理論與實務的能力。
This is an introductory course of deep learning that introduces the fundamentals and practices of deep learning. The course will start from a refresher in basics of machine learning, neural networks, to recent developments. Various deep learning architectures and their applications to computer vision, sequence modeling, and more will also be covered. Besides gaining fundamental knowledge of deep learning algorithms, students will get practical experience by implementing deep models via coding assignments and final projects using Python and deep learning frameworks.

課程目標 Objectives

了解深度學習模型如何運作、能利用深度學習去解決問題及具備實作能力
Students are expected to understand
- How deep learning works
- How to apply deep learning to solve problems
- How to use toolkit to implement learning models

授課方式 Teaching methods

課堂講授及報告
lecture and presentation

評分方式﹝評分標準及比例﹞Evaluation (Criteria and ratio)等第制單科成績對照表 letter grading reference

1.作業 Homework assignments：50%
2.期末專題 Final project：30%
3.其它 (出席、小考...) Others (attendance, quiz, ...)：20%

參考書/教科書/閱讀文獻 Reference book/ textbook/ documents
〔請遵守智慧財產權觀念，不可非法影印。教師所提供之教材供學生本人自修學習使用,不得散播及做為商業用途〕
No copies for intellectual property rights. Textbooks provided by the instructor used only for self-study, can not broadcast or commercial use

序號	作者	書名	出版社	出版年	出版地	ISBN#
No.	Auther	Title	Publisher	Year of publish	Publisher place	ISBN#
1	Ian Goodfellow, Yoshua Bengio, Aaron Courville	Deep Learning	MIT	2016		0387848576

彈性暨自主學習規劃 Alternative learning periods

本門課程是否有規劃實施學生彈性或自主學習內容（每1學分2小時）

Is any alternative learning periods planned for this course (with each credit corresponding to two hours of activity)?

否：教師需於「每週課程內容及預計進度」填寫18週課程進度（每1學分18小時之正課內容）。
No：The instructor will include an 18-week course plan in the weekly scheduled progress (each credit corresponds to 18 hours of instruction)

是：教師需於「每週課程內容及預計進度」填寫16週課程內容（每1學分16小時之正課內容），並於下列欄位填寫每1學分2小時學生彈性或自主學習內容。
Yes：The instructor will include a 16-week course plan in the weekly scheduled progress (each credit corresponds to 16 hours of instruction);the details of the planned alternative learning periods are provided below (each credit corresponds to two hours of activity).

學生彈性或自主學習活動 Alternative learning periods	勾選或填寫規劃內容 Place a check in the appropriate box or provide details	時數 Number of hours
學生分組實作及討論 Group work and discussion
參與課程相關作業、作品、實驗 Participation in course-related assignments, work, or experiments
參與校內外活動（研習營、工作坊、參訪）或競賽 Participation in on- or off-campus activities (e.g., seminars, workshops, and visits) or competitions
課外閱讀 Extracurricular reading
線上數位教材學習 Learning with online digital learning materials
其他（請填寫規劃內容） Other (please provide details)

每週課程內容及預計進度 Weekly scheduled progress

週次	日期	授課內容及主題
Week	Date	Content and topic
1	2019/09/09~2019/09/15	課程介紹 Course logistics and introduction
2	2019/09/16~2019/09/22	Python及線代回顧 Python and linear algebra review
3	2019/09/23~2019/09/29	機器學習簡介 Machine learning basics
4	2019/09/30~2019/10/06	深度學習簡介 Deep learning basics
5	2019/10/07~2019/10/13	國慶日 No class
6	2019/10/14~2019/10/20	深度學習簡介 Deep learning basics
7	2019/10/21~2019/10/27	卷積神經網路 Convolutional neural networks
8	2019/10/28~2019/11/03	卷積神經網路 Convolutional neural networks
9	2019/11/04~2019/11/10	遞歸神經網路 Recurrent neural networks
10	2019/11/11~2019/11/17	遞歸神經網路 Recurrent neural networks
11	2019/11/18~2019/11/24	注意力模型 Attention model
12	2019/11/25~2019/12/01	生成對抗網路 Generative neural networks
13	2019/12/02~2019/12/08	生成對抗網路 Generative neural networks
14	2019/12/09~2019/12/15	強化學習 Reinforcement learning
15	2019/12/16~2019/12/22	強化學習 Reinforcement learning
16	2019/12/23~2019/12/29	強化學習 Reinforcement learning
17	2019/12/30~2020/01/05	期末報告 Final project presentation
18	2020/01/06~2020/01/12	期末報告 Final project presentation

課業討論時間 Office hours

時段1 Time period 1:
時間 Time：星期三14:00 - 16:00
地點 Office/Laboratory：CM 4066
時段2 Time period 2：
時間 Time：星期四12:00 - 14:00
地點 Office/Laboratory：

系所學生專業能力/全校學生基本素養與核心能力 basic disciplines and core capabilitics of the dcpartment and the university

系所學生專業能力/全校學生基本素養與核心能力
basic disciplines and core capabilities of the department and the university

課堂活動與評量方式
Class activities and evaluation

本課程欲培養之能力與素養 This course enables students to achieve.

紙筆考試或測驗 Test.

課堂討論︵含個案討論︶ Group discussion (case analysis).

個人書面報告、作業、作品、實驗 Indivisual paper report/ assignment/ work or experiment.

群組書面報告、作業、作品、實驗 Group paper report/ assignment/ work or experiment.

個人口頭報告 Indivisual oral presentation.

群組口頭報告 Group oral presentation.

課程規劃之校外參訪及實習 Off-campus visit and intership.

證照/檢定 License.

參與課程規劃之校內外活動及競賽 Participate in off-campus/ on-campus activities and competitions.

課外閱讀 Outside reading.

※系所學生專業能力 Basic disciplines and core capabilities of the department

1.具備資訊倫理與社會責任實踐的能力 1. Awareness of information ethics.

2.具備溝通能力 2. Communication Skills.

3.具備解決資訊管理問題之能力 3. Capabilities to solve IT-related problems.

4.具備資訊管理之專業知識 4. Professional knowledge of information technology.

5.具備國際觀 5. Global perspective.

※全校學生基本素養與核心能力 Basic disciplines and core capabilities of the university

1.表達與溝通能力。 1. Articulation and communication skills

2.探究與批判思考能力。 2. Inquisitive and critical thinking abilities

3.終身學習能力。 3. Lifelong learning

4.倫理與社會責任。 4. Ethnics and social responsibility

5.美感品味。 5. Aesthetic appreciation

6.創造力。 6. Creativity

7.全球視野。 7. Global perspective

8.合作與領導能力。 8. Team work and leadership

9.山海胸襟與自然情懷。 9. Broad-mindedness and the embrace of nature　

本課程與SDGs相關項目：The course relates to SDGs items:

尚未建立SDGS資料

本課程校外實習資訊: This course is relevant to internship:

本課程無註記包含校外實習