| 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.
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| topic Discussion / Production |
| Exercises |
| Lecturing |
| Discussion |
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| Written Presentation |
| Assignment |
| Internship |
| Oral Presentation |
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| 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)/ HW #4 |
| Week 16 |
Some Deep Learning Examples for image processing (Chap 8)
Term Project
Term Project |
self-directed
learning |
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| Evaluation |
Homework: 40%
Term Project: 60% |
| 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 |
| Wednesday 5:00pm – 6:00pm or by appointment |
| Sustainable Development Goals, SDGs(Link URL) |
| 09.Industry, Innovation and Infrastructure | include experience courses：N |
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