NCHU Course Outline

Course Name	(中) 深度學習(7754)
	(Eng.) Deep learning
Offering Dept	Executive Master Program in Big Data
Course Type	Elective			Credits	3	Teacher	TSAI, HUNG-HSU
Department	Executive Master Program in Big Data / (W)Graduate			Language	Chinese	英文/EMI		Semester	2025-SPRING
Course Description	1.簡介深度學習發展(Introduction to deep learning) 2.概述深度學習與人工智慧、機器學習、類神經網路(Introduction to artificial intelligence, deep learning, machine learning and neural network) 3.簡介機器學習、類神經網路之學習理論及學習演算法(Introduction to learning theory and algorithm to machine learning and neural network) 4.實務練習運用機器學習、類神經網路之學習建模模型(Practice for using machine learning and artificial neural network in building learning models) 5.簡介深度學習-卷積類神經網路及實務練習卷積類神經網路之建模及應用(Deep learning : Introduction to convolutional neural network and its applications) 6.簡介深度學習-遞歸神經網路和長短期記憶模型及實務練習長短期記憶模型之建模及應用(Deep learning : Introduction to Recurrence neural network and long-short-term memory model and their applications)
Prerequisites								self-directed learning in the course	Y

Relevance of Course Objectives and Core Learning Outcomes(%)			Teaching and Assessment Methods for Course Objectives
Course Objectives	Competency Indicators	Ratio(%)	Teaching Methods	Assessment Methods
1.學會深度學習與機器學習、類神經網路之基礎學習理論 (learn basic learning theories for machine learning and artificial neural network) 2.學會以機器學習及類神經網路來建模應用於分類模型設計與實作(learn how to adopt machine learning and artificial neural network to build learning models for the design of classifiers) 3.學會以深度學習-卷積類神經網路來建模應用於分類模型設計與實作(learn how to adopt the convolutional neural network of deep learning to build learning models for the practical applications ) 4.學會以實務練習-長短期記憶模型來建模應用於預測模型設計與實作(learn how to adopt the long-short-term memory of deep learning to build learning models for the practical applications)			Lecturing Exercises Discussion	Written Presentation Attendance Oral Presentation Assignment Internship
Course Content and Homework/Schedule/Tests Schedule
Week Course Content Week 1 第1週: 說明課程大綱、簡述深度學習、說明實作環境。 (Week #1: Introduction of syllabus, deep learning, building environment of running deep learning programs) Week 2 第2週: 人工智慧概述 (Week #2: Introduction of Artificial Intelligence) Week 3 第3-4週: 機器學習概述及應用實作 (Week #3-#4: Introduction of machine learning and its applications) Week 4 第3-4週: 機器學習概述及應用實作 (Week #3-#4: Introduction of machine learning and its applications) Week 5 第4-6週:類神經網路概述及應用實作 (Week #4-#6: Introduction of artificial neural network and its applications) Week 6 第4-6週:類神經網路概述及應用實作 (Week #4-#6: Introduction of artificial neural network and its applications) Week 7 第6-7週: 基礎深度學習-卷積類神經網路來建模應用於分類模型設計與實作 (Week #6-#7:Introduction of convolution neural network and its applications) Week 8 第8-9週: 深度學習-卷積類神經網路來建模應用於分類模型設計與實作 (Week #8-#9:Introduction of convolution neural network and its applications) Week 9 第8-9週: 深度學習-卷積類神經網路來建模應用於分類模型設計與實作 (Week #8-#9:Introduction of convolution neural network and its applications) Week 10 第10-11週 期中報告 (Week #10-#11: midterm report) Week 11 第10-11週 期中報告 (Week #10-#11: midterm report) Week 12 第12週: 深度學習-長短期記憶模型來建模應用於預測模型設計與實作 (Week #12:Introduction of long-short-term memory and its applications) Week 13 第13-14週: 論文回顧: 深度學習應用專題-影像分類、影像檢索、影像分割 (Week #13-#14: Special topics for deep learning in applications such as image classification, image retrieval, and image segmentation) Week 14 第13-14週: 論文回顧: 深度學習應用專題-影像分類、影像檢索、影像分割 (Week #13-#14: Special topics for deep learning in applications such as image classification, image retrieval, and image segmentation) Week 15 第15-16週 期末報告(Week #15-#16: final-term report) Week 16 第15-16週 期末報告(Week #15-#16: final-term report) Week 17 第17-18週 期末報告(Week #17-#18: final-term report)(Self-learning) Week 18 第17-18週 期末報告(Week #17-#18: final-term report)(Self-learning)
Evaluation
1-期中報告(midterm report)(15%)、2-期末報告(final-term report)(40%)、3-participation(15%)、4-電腦作業(Computer Assignment) (10%) 6-Paper Study & Presentation (one for each; midterm & Final team) (20%) 7-extra credits (20%)
Textbook & other References
Textbook: 自編 References: 1. Python機器學習第三版（上）博碩 作者：Sebastian Raschka、Vahid Mirjalili著 2. Python機器學習第三版（下）博碩 作者：Sebastian Raschka、Vahid Mirjalili　著
Teaching Aids & Teacher's Website
自編
Office Hours
(一)17:30-18:30
Sustainable Development Goals, SDGs(Link URL)
04.Quality Education include experience courses：N

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Update Date, year/month/day：2025/02/17 17:52:10	Printed Date, year/month/day：2025 / 5 / 10
The second-hand book website：http://www.myub.com.tw/