NCHU Course Outline
Course Name (中) 深度學習(4269)
(Eng.) Deep learning
Offering Dept Bachelor Program in Electrical Engineering and Computer Science
Course Type Elective Credits 3 Teacher LIU TSUNG JUNG
Department Bachelor Program in Electrical Engineering and Computer Science/Undergraduate Language 中/英文 Semester 2024-FALL
Course Description This course will allow students to understand data representation and mathematical models in-depth while cultivating their skills in translating mathematical thinking into practical code. Moreover, by learning the fundamentals and recent advancements in deep learning, students will be able to apply deep learning techniques to solve complex real-world problems with the expectation that they can contribute to the development of artificial intelligence.
Prerequisites
self-directed learning in the course N
Relevance of Course Objectives and Core Learning Outcomes(%) Teaching and Assessment Methods for Course Objectives
Course Objectives Competency Indicators Ratio(%) Teaching Methods Assessment Methods
This course is concerned with the following objectives:
• Understanding data representation and underlying mathematical formulas for deep learning: We will delve into various data representations, such as vectors, matrices, tensors, etc., and explore the mathematical principles and applications behind these representations.
• Translating mathematical formulas of deep learning into code: We will learn how to transform mathematical formulas into code to achieve specific tasks. It involves selecting appropriate models and writing efficient and reliable code. Additionally, we will learn how to evaluate and interpret the results to verify their correctness and effectiveness.
• Learning the fundamentals and recent advancements in deep learning: Deep learning is a significant branch of artificial intelligence that emulates the structure and functionality of the human brain, enabling automatic learning and extraction of critical features from data. This course will teach students basic deep learning concepts, including various neural networks, forward/backward propagations, etc. We will also explore the latest developments in deep learning, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), and understand their applications in image processing.
topic Discussion/Production
Exercises
Discussion
Lecturing
Written Presentation
Attendance
Oral Presentation
Assignment
Course Content and Homework/Schedule/Tests Schedule
Week Course Content
Week 1 Basic introduction to artificial intelligence (Chap 1)
Week 2 Introduction to Environmental and Data Science Packages (Chap 2)
Week 3 Introduction to Environmental and Data Science Packages (Chap 2)
Week 4 Introduction to Environmental and Data Science Packages (Chap 2)
Week 5 Machine Learning and Deep Learning Fundamentals (Chap 3)
Week 6 Machine Learning and Deep Learning Fundamentals (Chap 3)
Week 7 Machine Learning and Deep Learning Fundamentals (Chap 3)
Week 8 Neural Networks and Convolutional Neural Networks (Chap 4)
Week 9 Neural Networks and Convolutional Neural Networks (Chap 4)/ HW #1
Week 10 Common Deep Learning Training Techniques (Chap 5)
Week 11 Introduction to Deep Learning Architectures (Chap 6)
Week 12 Advanced Deep Learning Techniques (Chap 7)/ HW #2
Week 13 Advanced Deep Learning Techniques (Chap 7)/ HW #3
Week 14 Some Deep Learning Examples for image processing (Chap 8)
Week 15 Some Deep Learning Examples for image processing (Chap 8)
Week 16 Some Deep Learning Examples for image processing (Chap 8)
Week 17 Term Project
Week 18 Term Project
Evaluation
Homework: 30%
Term Project: 60%
Attendance: 10%
Textbook & other References
Textbook
彭彥璁,李偉華,陳彥蓉, “深度學習–影像處理應用”, 初版, 全華圖書, 2023

Reference book
Sebastian Raschka, Yuxi (Hayden) Liu, and Vahid Mirjalili, “Machine Learning with PyTorch and Scikit-Learn”, 1st edition, Packt Publishing, 2022.
Teaching Aids & Teacher's Website
iLearning 3.0
Office Hours
Thursday 5:00pm – 6:00pm or by appointment
Sustainable Development Goals, SDGs
09.Industry, Innovation and Infrastructureinclude experience courses：N
Please respect the intellectual property rights and use the materials legally.Please repsect gender equality.
Update Date, year/month/day：2024/09/15 12:31:33 Printed Date, year/month/day：2024 / 11 / 21
The second-hand book website：http://www.myub.com.tw/