課程名稱 |
深度學習於電腦視覺 Deep Learning for Computer Vision |
開課學期 |
111-1 |
授課對象 |
電機資訊學院 電信工程學研究所 |
授課教師 |
王鈺強 |
課號 |
CommE5052 |
課程識別碼 |
942 U0660 |
班次 |
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學分 |
3.0 |
全/半年 |
半年 |
必/選修 |
選修 |
上課時間 |
星期二2,3,4(9:10~12:10) |
上課地點 |
博理112 |
備註 |
總人數上限:100人 |
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課程簡介影片 |
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核心能力關聯 |
核心能力與課程規劃關聯圖 |
課程大綱
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課程概述 |
Computer vision has become ubiquitous in our society, with a variety of applications in image/video search and understanding, medicine, drones, and self-driving cars. As the core to many of the above applications, visual analysis such as image classification, segmentation, localization and detection would be among the well-known problems in computer vision. Recent developments in neural networks (a.k.a. deep learning) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into the details of deep learning architectures, with a particular focus on understanding and designing learnable models for solving various vision tasks. |
課程目標 |
?This course will expose students to cutting-edge research — starting from a refresher in basics of machine learning, computer vision, neural networks, to recent developments. Each topic will begin with instructor lectures to present context and background material, followed by discussions and homework assignments, allowing the students to develop hand-on experiences on deep learning techniques for solving practical computer vision problems. |
課程要求 |
Engineering Mathematics (e.g., linear algebra, probability, etc.), Machine Learning (strongly suggested but optional) |
預期每週課後學習時數 |
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Office Hours |
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指定閱讀 |
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參考書目 |
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評量方式 (僅供參考) |
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週次 |
日期 |
單元主題 |
Week 1 |
09/06 |
Course logistics & registration; Machine Learning 101 |
Week 2 |
09/13 |
Introduction to Convolutional Neural Networks (I) |
Week 3 |
09/20 |
Introduction to Convolutional Neural Networks (II)
Tutorials on Python, Github, etc. (by TAs) |
Week 4 |
09/27 |
Object Detection & Segmentation; Generative Model |
Week 5 |
10/04 |
Generative Adversarial Networks, and Diffusion Model |
Week 6 |
10/11 |
Transfer Learning for Visual Classification & Synthesis |
Week 7 |
10/18 |
Guest Lecture (TBD) |
Week 8 |
10/25 |
Recurrent Neural Networks |
Week 9 |
11/01 |
Transformer; Vision & Language (I) |
Week 10 |
11/08 |
Vision & Language (II); Few-Shot Learning (I) |
Week 11 |
11/15 |
N/A |
Week 12 |
11/22 |
3D Vision |
Week 13 |
11/29 |
Announcement of Final Project |
Week 14 |
12/06 |
Self-Supervised Learning & Guest Lecture |
Week 15 |
12/13 |
Federated Learning, Domain Generalization and More Advanced Topics |
Week 17 |
12/29 Thur |
Presentation for Final Projects |
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