查詢教學大綱與進度

教學大綱與進度

課程基本資料：

學年期課號課程名稱階段學分時數修教師班級人撤備註

110-1 294663 軟性計算 1 3.0 3 ★ 陳俊豪資財所 11 1 ◎科研243教室

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
110-1	294663	軟性計算	1	3.0	3	★	陳俊豪	資財所	11	1	◎科研243教室

教學大綱與進度：

教師姓名陳俊豪

Email chchen6814@gmail.com

最後更新時間 2021-09-16 01:26:06

課程大綱本課程將介紹軟性計算的概念、特性與優點，旨在提供學生多種不同的技術解決相關問題。課程目標有二：(1) 相關軟性計算技術，包含：關聯分析、群聚分析、分類等；(2)相關軟性計算技術應用，包含：時間序列分析、M-V Model、商業資料分析與組合管理等。 In this course, concepts, properties advantages of soft computing are introduced for providing various ways for students to solve problems. The goals of this course include two parts: (1) related soft computing techniques, including association analysis, clustering, classification, etc; (2) related soft computing applications, including time series analysis, mean-variance model, business data analysis and portfolio management, etc.

課程進度 Week 1: Soft Computing Overview Week 2: Soft Computing Technique - Introduction to Association Analysis #1 Week 3: Soft Computing Technique - Advanced Association Analysis Week 4: Soft Computing Technique - Introduction to Clustering Analysis #2 Week 5: Soft Computing Technique - Advanced Clustering Analysis Week 6: Soft Computing Technique - Introduction to Classification #3 Week 7: Soft Computing Technique - Invited Talk I ( Prof. Jerry C.W. Lin) Week 8: Soft Computing Technique - Introduction to Genetic Algorithms #4 Week 9: Midterm Examination Week 10: Advanced Genetic Algorithms Week 11: Invited Talk II Week 12: Invited Talk III Week 13: Portfolio Management (M-V Model and its Optimization) Week 14: Case Discussion & Paper Study #5 Week 15: Case Discussion & Paper Study #6 Week 16: Case Discussion & Paper Study Week 17: Final Project Presentation Week 18: Final Examination

評量方式與標準 Attendance：20％ Mark of usual：20％ Homework：20% Paper study：20% Project Presentation: 20%

使用教材、參考書目或其他 【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】

使用外文原文書：否
Python for Finance (Analyze Big Financial Data, Yves Hilpisch, O'Reilly Media, 2014. Deep Learning, Ian Goodfellow, Yoshua Bengio and Aaron Courville Data Mining : Concepts and Techniques, 3/e (Hardcover), Jiawei Han, Micheline Kamber, Jian Pei, 2011.

課程諮詢管道 If you have any question, let me know by email.

課程對應SDGs指標

課程是否導入AI

備註 ●上課方式：遠距上課

●評量方式：
Attendance：20％
Mark of usual：20％
Homework：20%
Paper study：20%
Project Presentation: 20%
●補充說明資訊：
因應疫情警戒等級調整之上課方式。
Google Meet Link: meet.google.com/xfd-txyp-uoj
ps: please login before the 4:10AM

教師姓名	陳俊豪
Email	chchen6814@gmail.com
最後更新時間	2021-09-16 01:26:06
課程大綱	本課程將介紹軟性計算的概念、特性與優點，旨在提供學生多種不同的技術解決相關問題。課程目標有二：(1) 相關軟性計算技術，包含：關聯分析、群聚分析、分類等；(2)相關軟性計算技術應用，包含：時間序列分析、M-V Model、商業資料分析與組合管理等。 In this course, concepts, properties advantages of soft computing are introduced for providing various ways for students to solve problems. The goals of this course include two parts: (1) related soft computing techniques, including association analysis, clustering, classification, etc; (2) related soft computing applications, including time series analysis, mean-variance model, business data analysis and portfolio management, etc.
課程進度	Week 1: Soft Computing Overview Week 2: Soft Computing Technique - Introduction to Association Analysis #1 Week 3: Soft Computing Technique - Advanced Association Analysis Week 4: Soft Computing Technique - Introduction to Clustering Analysis #2 Week 5: Soft Computing Technique - Advanced Clustering Analysis Week 6: Soft Computing Technique - Introduction to Classification #3 Week 7: Soft Computing Technique - Invited Talk I ( Prof. Jerry C.W. Lin) Week 8: Soft Computing Technique - Introduction to Genetic Algorithms #4 Week 9: Midterm Examination Week 10: Advanced Genetic Algorithms Week 11: Invited Talk II Week 12: Invited Talk III Week 13: Portfolio Management (M-V Model and its Optimization) Week 14: Case Discussion & Paper Study #5 Week 15: Case Discussion & Paper Study #6 Week 16: Case Discussion & Paper Study Week 17: Final Project Presentation Week 18: Final Examination
評量方式與標準	Attendance：20％ Mark of usual：20％ Homework：20% Paper study：20% Project Presentation: 20%
使用教材、參考書目或其他	【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】使用外文原文書：否 Python for Finance (Analyze Big Financial Data, Yves Hilpisch, O'Reilly Media, 2014. Deep Learning, Ian Goodfellow, Yoshua Bengio and Aaron Courville Data Mining : Concepts and Techniques, 3/e (Hardcover), Jiawei Han, Micheline Kamber, Jian Pei, 2011.
課程諮詢管道	If you have any question, let me know by email.
課程對應SDGs指標
課程是否導入AI
備註	●上課方式：遠距上課 ●評量方式： Attendance：20％ Mark of usual：20％ Homework：20% Paper study：20% Project Presentation: 20% ●補充說明資訊：因應疫情警戒等級調整之上課方式。 Google Meet Link: meet.google.com/xfd-txyp-uoj ps: please login before the 4:10AM