113學年度 資訊工程學系人工智慧碩士班 課程規劃表
113 Academic year Curriculum planning table
一、最低畢業學分數(Minimum graduation credits)：
- 畢業學分(Graduation credits)：30.0學分(credits)
- 專業必修(Professional Required)：6.0學分(credits)
- 專業選修(Professional elective)：24.0學分(credits)
- Minimum Graduation Credits: Students must earn at least 30 credits to graduate, including:
* Professional Required Courses: 6 credits
* Professional Elective Courses: 24 credits
二、重要相關規定(Important regulations)：
| 1. 本碩士班學生修習專業選修課程需包含「人工智慧課程類5選2」至少6學分；專業選修內修習本碩士班開設之專業選修課程至少9學分，其餘9學分可選擇其他研究所之專業選修課程。 2. 碩士生修習資訊工程學系碩博士班之專業選修科目均得列入畢業學分。 3. 申請抵免學分以專業選修課程學分數二分之一為限，本系開設之課程者不在此限。抵免時程以學校規定辦理。 4. 碩士生每學期必須修習「專題講座」課程，修滿(一)、(二)、(三)、(四)或直至畢業為止，但除(一)、(二)之外，所修學分不列入畢業學分。五年修讀學、碩士生請另行參照本系五年修讀學、碩士學位實施細則。 5. 碩士生每學期必須修習「論文研究」課程，畢業學分至多採計2次4學分。 6. 碩士生需通過英文資格認定方可畢業。相關英文課程之認定，請參考「資訊工程學系研究所英語能力畢業規定」。 7. 本系每位同學(含雙主修同學)均必須選修「程式設計能力檢定」課程並取得成績等第，惟此成績等第之高低不影響畢業。 8. 凡以各類獎學金入學之受獎學生，除了本課規之外，另須依該獎學金之相關規定辦法修課及完成學業。 9. 本學年度起入學之本系研究所學生，須於入學後之第一學期選課期間，至系辦登記修習「臺灣學術倫理教育資源中心」線上平台之「學術研究倫理教育課程」，須通過線上課程測驗成績達及格標準，並出示修課證明始得申請學位考試。未通過者，須於申請學位考試前補修完成，未完成本課程者，不得申請學位考試。 10.為推動全英語授課，理工學院EMI教師所開設之EMI課程，與本系相同課名或相同性質課程可視為等同課程。請參閱相同或等同課程對照表。 |
| 1. Departmental Course Requirement At least 50% of the courses taken must be offered by the Department of Computer Science and Information Engineering (CSIE). 2. Eligibility of Graduate-Level Courses Master’s students may take graduate-level courses offered by both the Master’s and Ph.D. programs within CSIE. All such courses are eligible for inclusion in graduation credits. 3. Credit Transfer Limitations Master’s students can transfer up to 50% of the professional elective course credits. Courses offered by the Department of Computer Science and Information Engineering (CSIE) are not included in this limit. Credit transfers must follow the university’s schedule. 4. Special Topic Lecture Requirement Master’s students must enroll in “Special Topic Lecture” courses—(I), (II), (III), or (IV)—every semester until graduation. Only the credits earned from “Special Topic Lecture (I)” and “(II)” count toward graduation credits. Students in the Five-Year Bachelor-Master Program should refer to the department’s Five-Year Bachelor-Master Degree Implementation Guidelines (本系五年修讀學、碩士學位實施細則) for additional details. 5. Thesis Requirement Master’s students must register for the “Thesis” course every semester until graduation. A maximum of 4 credits from two “Thesis” courses may be counted toward graduation credits. 6. English Proficiency Requirement Master’s students must meet the department’s English Proficiency Requirement for Graduate Students (資訊工程學系研究生英語能力畢業規定) to be eligible for graduation. 7. Programming Ability Certification All students, including those with double majors, must complete the Programming Ability Certification Course and receive a grade. However, the grade will not affect graduation eligibility. 8. Scholarship Obligations Scholarship recipients must adhere to the scholarship’s specific regulations and guidelines in addition to completing the curriculum requirements. 9. Academic Research Ethics Education Certification Requirement Master’s students admitted from this academic year onwards must register for the Online Courses for Academic Research Ethics Education (學術研究倫理教育課程) provided by the Center for Taiwan Academic Research Ethics Education (臺灣學術倫理教育資源中心) during the course selection period of their first semester. Registration must be completed through the CSIE Department Office. Students must pass the online course assessment and obtain the certification of completion to be eligible to apply for the Oral Defense Exam. Failure to complete this requirement will disqualify students from applying for the exam. 10. EMI Course Equivalency To promote English as a Medium of Instruction (EMI), EMI courses offered by the College of Science and Engineering with the same name or nature as those in this program will be considered equivalent. For details, refer to the NDHU Course Equivalency Table (國立東華大學相同或等同課程對照表). |
| 科目名稱 Course Title(Chinese) | 英文科目名稱 Course Title(English) | 科目代碼 Course Number | 學分 Credit | *先修科目或#背景科目 *Prerequisite course or #Background course | 備註 Remarks |
| 專業必修(required) | |||||
| 論文研究 | Thesis | AIIA50000 | 2.0 | 每學期必修 | |
| 專題講座(一) | Special Topic Lecture (Ⅰ) | AIIA50010 | 1.0 | 碩一必修 | |
| 專題講座(二) | Special Topic Lecture (Ⅱ) | AIIA50170 | 1.0 | 碩一必修 | |
| 專業選修(elective) | |||||
| 影像處理 | Image Processing | AIIA50150 | 3.0 | ||
| 高等電腦視覺 | Advanced Computer Vision | AIIA50160 | 3.0 | ||
| 柔性計算系統實務 | Realization of Soft Computing Systems | AIIA50250 | 3.0 | ||
| 高等資訊檢索 | Advanced Information Retrieval | CSIE@0850 | 3.0 | ||
| 大數據分析 | Big Data Analytics | AIIA50060 | 3.0 | ||
| 圖型識別 | Pattern Recognition | AIIA50260 | 3.0 | ||
| 智慧型系統設計 | Intelligent System Design | AIIA50130 | 3.0 | ||
| 大數據系統 | Big Data Systems | AIIA50050 | 3.0 | ||
| 最佳化方法與應用 | Optimization methods and applications | AIIA50020 | 3.0 | ||
| 電腦視覺與機器學習 | Machine Learning in Computer Vision | CSIE@0870 | 3.0 | ||
| 推薦系統 | Recommender System | AIIA50070 | 3.0 | ||
| 智慧異質無線網路資源管理 | Intelligent Resource Management for Heterogeneous Wireless Networks | CSIE@0880 | 3.0 | ||
| 最佳化與決策專題 | Topics on optimization and decision making | AIIA50200 | 3.0 | ||
| 智慧物聯網技術與應用 | Intelligent IoT technologies and applications | AIIA50100 | 3.0 | ||
| 社群網路與推薦系統 | Social Networks and recommender systems | AIIA50090 | 3.0 | ||
| 實務程式設計與應用 | Pragmatic Programming and Applications | AIIA50210 | 3.0 | ||
| 臨床醫學與智慧醫療照護 | Clinical Medicine and Smart Healthcare | AIIA50240 | 3.0 | ||
| 高互動多媒體設計之研究 | Highly interactive multimedia design | AIIA50180 | 3.0 | ||
| 電腦對局理論 | Theory of Computer Games | AIIA50190 | 3.0 | ||
| 程式設計能力檢定 | Programming Ability Certification | AIIA50110 | 0.0 | ||
| *四、其他 | / | CSIE@0920 | 3.0 | ||
| 專題講座(三) | Special Topic Lecture (Ⅲ) | AIIA50220 | 1.0 | 碩二必修 | |
| 專題講座(四) | Special Topic Lecture (Ⅳ) | AIIA50230 | 1.0 | 碩二必修 | |
| 科技英文寫作 | Science and Technical Writing | CSIE@0950 | 3.0 | ||
| 以下科目 5 選 2，至少需修習 6.0 學分(select 2 lectures from 5 lectures, at least 6.0 credits) | |||||
| 人工智慧 | Artifical Intelligence | AIIA50030 | 3.0 | ||
| 機器學習 | Machine Learning | AIIA50120 | 3.0 | ||
| 資料探勘 | Data Mining | AIIA50040 | 3.0 | ||
| 深度學習基石與實務 | Foundation and Practice of Deep Learning | AIIA50140 | 3.0 | ||
| 前瞻機器學習原理與技術 | Advanced Machine Learning Principles and Technology | AIIA50080 | 3.0 | ||